Multimodal & Large Language Models

Note: This paper list is only used to record papers I read in the daily arxiv for personal needs. If you find I missed some important and exciting work, it would be super helpful to let me know. Thanks!

Table of Contents

• Survey
• Position Paper
• Structure
• Planning
• Reasoning
• Generation
• Representation Learning
• LLM Analysis
• LLM Safety
• LLM Evaluation
• LLM Reasoning
• LLM Application
• LLM with Memory
• LLM with Human
• LLM Foundation
• Scaling Law
• LLM Data Engineering
• VLM Data Engineering
• Alignment
• Scalable Oversight&SuperAlignment
• RL Foundation
• Beyond Bandit
• Agent
• Interaction
• Critique Modeling
• MoE/Specialized
• Vision-Language Foundation Model
• Vision-Language Model Analysis & Evaluation
• Vision-Language Model Application
• Multimodal Foundation Model
• Image Generation
• Document Understanding
• Tool Learning
• Instruction Tuning
• Incontext Learning
• Learning from Feedback
• Video Foundation Model
• Key Frame Detection
• Pretraining
• Vision Model
• Adaptation of Foundation Model
• Prompting
• Efficiency
• Analysis
• Grounding
• VQA Task
• VQA Dataset
• Social Good
• Application
• Benchmark & Evaluation
• Dataset
• Robustness
• Hallucination&Factuality
• Cognitive NeuronScience & Machine Learning
• Theory of Mind
• Cognitive NeuronScience
• World Model
• Resource

Survey

• Multimodal Learning with Transformers: A Survey; Peng Xu, Xiatian Zhu, David A. Clifton
• Multimodal Machine Learning: A Survey and Taxonomy; Tadas Baltrusaitis, Chaitanya Ahuja, Louis-Philippe Morency; Introduce 4 challenges for multi-modal learning, including representation, translation, alignment, fusion, and co-learning.
• FOUNDATIONS & RECENT TRENDS IN MULTIMODAL MACHINE LEARNING: PRINCIPLES, CHALLENGES, & OPEN QUESTIONS; Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency
• Multimodal research in vision and language: A review of current and emerging trends; Shagun Uppal et al;
• Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods; Aditya Mogadala et al
• Challenges and Prospects in Vision and Language Research; Kushal Kafle et al
• A Survey of Current Datasets for Vision and Language Research; Francis Ferraro et al
• VLP: A Survey on Vision-Language Pre-training; Feilong Chen et al
• A Survey on Multimodal Disinformation Detection; Firoj Alam et al
• Vision-Language Pre-training: Basics, Recent Advances, and Future Trends; Zhe Gan et al
• Deep Multimodal Representation Learning: A Survey; Wenzhong Guo et al
• The Contribution of Knowledge in Visiolinguistic Learning: A Survey on Tasks and Challenges; Maria Lymperaiou et al
• Augmented Language Models: a Survey; Grégoire Mialon et al
• Multimodal Deep Learning; Matthias Aßenmacher et al
• Sparks of Artificial General Intelligence: Early experiments with GPT-4; Sebastien Bubeck et al
• Retrieving Multimodal Information for Augmented Generation: A Survey; Ruochen Zhao et al
• Is Prompt All You Need? No. A Comprehensive and Broader View of Instruction Learning; Renze Lou et al
• A Survey of Large Language Models; Wayne Xin Zhao et al
• Tool Learning with Foundation Models; Yujia Qin et al
• A Cookbook of Self-Supervised Learning; Randall Balestriero et al
• Foundation Models for Decision Making: Problems, Methods, and Opportunities; Sherry Yang et al
• Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation; Patrick Fernandes et al
• Reasoning with Language Model Prompting: A Survey; Shuofei Qiao et al
• Towards Reasoning in Large Language Models: A Survey; Jie Huang et al
• Beyond One-Model-Fits-All: A Survey of Domain Specialization for Large Language Models; Chen Ling et al
• Unifying Large Language Models and Knowledge Graphs: A Roadmap; Shirui Pan et al
• Interactive Natural Language Processing; Zekun Wang et al
• A Survey on Multimodal Large Language Models; Shukang Yin et al
• TRUSTWORTHY LLMS: A SURVEY AND GUIDELINE FOR EVALUATING LARGE LANGUAGE MODELS’ ALIGNMENT; Yang Liu et al
• Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback; Stephen Casper et al
• Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies; Liangming Pan et al
• Challenges and Applications of Large Language Models; Jean Kaddour et al
• Aligning Large Language Models with Human: A Survey; Yufei Wang et al
• Instruction Tuning for Large Language Models: A Survey; Shengyu Zhang et al
• From Instructions to Intrinsic Human Values —— A Survey of Alignment Goals for Big Models; Jing Yao et al
• A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation; Xiaowei Huang et al
• Explainability for Large Language Models: A Survey; Haiyan Zhao et al
• Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models; Yue Zhang et al
• Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity; Cunxiang Wang et al
• ChatGPT’s One-year Anniversary: Are Open-Source Large Language Models Catching up?; Hailin Chen et al
• Vision-Language Instruction Tuning: A Review and Analysis; Chen Li et al
• The Mystery and Fascination of LLMs: A Comprehensive Survey on the Interpretation and Analysis of Emergent Abilities; Yuxiang Zhou et al
• Efficient Large Language Models: A Survey; Zhongwei Wan et al
• The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision); Zhengyuan Yang et al
• Igniting Language Intelligence: The Hitchhiker’s Guide From Chain-of-Thought Reasoning to Language Agents; Zhuosheng Zhang et al
• Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis; Yafei Hu et al
• Multimodal Foundation Models: From Specialists to General-Purpose Assistants; Chunyuan Li et al
• A Survey on Large Language Model based Autonomous Agents; Lei Wang et al
• Video Understanding with Large Language Models: A Survey; Yunlong Tang et al
• A Survey of Preference-Based Reinforcement Learning Methods; Christian Wirth et al
• AI Alignment: A Comprehensive Survey; Jiaming Ji et al
• A SURVEY OF REINFORCEMENT LEARNING FROM HUMAN FEEDBACK; Timo Kaufmann et al
• TRUSTLLM: TRUSTWORTHINESS IN LARGE LANGUAGE MODELS; Lichao Sun et al
• AGENT AI: SURVEYING THE HORIZONS OF MULTIMODAL INTERACTION; Zane Durante et al
• Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: A Short Survey; Cedric Colas et al
• Safety of Multimodal Large Language Models on Images and Text; Xin Liu et al
• MM-LLMs: Recent Advances in MultiModal Large Language Models; Duzhen Zhang et al
• Rethinking Interpretability in the Era of Large Language Models; Chandan Singh et al
• Large Multimodal Agents: A Survey; Junlin Xie et al
• A Survey on Data Selection for Language Models; Alon Albalak et al
• What Are Tools Anyway? A Survey from the Language Model Perspective; Zora Zhiruo Wang et al
• Best Practices and Lessons Learned on Synthetic Data for Language Models; Ruibo Liu et al
• A Survey on the Memory Mechanism of Large Language Model based Agents; Zeyu Zhang et al
• A Survey on Self-Evolution of Large Language Models; Zhengwei Tao et al
• When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models; Xianzheng Ma et al

Position Paper

• Eight Things to Know about Large Language Models; Samuel R. Bowman et al
• A PhD Student’s Perspective on Research in NLP in the Era of Very Large Language Models; Oana Ignat et al
• Brain in a Vat: On Missing Pieces Towards Artificial General Intelligence in Large Language Models; Yuxi Ma et al
• Towards AGI in Computer Vision: Lessons Learned from GPT and Large Language Models; Lingxi Xie et al
• A Path Towards Autonomous Machine Intelligence; Yann LeCun et al
• GPT-4 Can’t Reason; Konstantine Arkoudas et al
• Cognitive Architectures for Language Agents; Theodore Sumers et al
• Large Search Model: Redefining Search Stack in the Era of LLMs; Liang Wang et al
• PROAGENT: FROM ROBOTIC PROCESS AUTOMATION TO AGENTIC PROCESS AUTOMATION; Yining Ye et al
• Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning; Zhiting Hu et al
• A Roadmap to Pluralistic Alignment; Taylor Sorensen et al
• Towards Unified Alignment Between Agents, Humans, and Environment; Zonghan Yang et al
• Video as the New Language for Real-World Decision Making; Sherry Yang et al
• A Mechanism-Based Approach to Mitigating Harms from Persuasive Generative AI; Seliem El-Sayed et al

Structure

• Finding Structural Knowledge in Multimodal-BERT; Victor Milewski et al
• Going Beyond Nouns With Vision & Language Models Using Synthetic Data; Paola Cascante-Bonilla et al
• Measuring Progress in Fine-grained Vision-and-Language Understanding; Emanuele Bugliarello et al
• PV2TEA: Patching Visual Modality to Textual-Established Information Extraction; Hejie Cui et al

Event Extraction

• Cross-media Structured Common Space for Multimedia Event Extraction; Manling Li et al; Focus on image-text event extraction. A new benchmark and baseline are proposed.
• Visual Semantic Role Labeling for Video Understanding; Arka Sadhu et al; A new benchmark is proposed.
• GAIA: A Fine-grained Multimedia Knowledge Extraction System; Manling Li et al; Demo paper. Extract knowledge (relation, event) from multimedia data.
• MMEKG: Multi-modal Event Knowledge Graph towards Universal Representation across Modalities; Yubo Ma et al

Situation Recognition

• Situation Recognition: Visual Semantic Role Labeling for Image Understanding; Mark Yatskar et al; Focus on image understanding. Given images, do the semantic role labeling task. No text available. A new benchmark and baseline are proposed.
• Commonly Uncommon: Semantic Sparsity in Situation Recognition; Mark Yatskar et al; Address the long-tail problem.
• Grounded Situation Recognition; Sarah Pratt et al
• Rethinking the Two-Stage Framework for Grounded Situation Recognition; Meng Wei et al
• Collaborative Transformers for Grounded Situation Recognition; Junhyeong Cho et al

Scene Graph

• Action Genome: Actions as Composition of Spatio-temporal Scene Graphs; Jingwei Ji et al; Spatio-temporal scene graphs (video).
• Unbiased Scene Graph Generation from Biased Training; Kaihua Tang et al
• Visual Distant Supervision for Scene Graph Generation; Yuan Yao et al
• Learning to Generate Scene Graph from Natural Language Supervision; Yiwu Zhong et al
• Weakly Supervised Visual Semantic Parsing; Alireza Zareian, Svebor Karaman, Shih-Fu Chang
• Scene Graph Prediction with Limited Labels; Vincent S. Chen, Paroma Varma, Ranjay Krishna, Michael Bernstein, Christopher Re, Li Fei-Fei
• Neural Motifs: Scene Graph Parsing with Global Context; Rowan Zellers et al
• Fine-Grained Scene Graph Generation with Data Transfer; Ao Zhang et al
• Towards Open-vocabulary Scene Graph Generation with Prompt-based Finetuning; Tao He et al
• COMPOSITIONAL PROMPT TUNING WITH MOTION CUES FOR OPEN-VOCABULARY VIDEO RELATION DETECTION; Kaifeng Gao et al; Video.
• LANDMARK: Language-guided Representation Enhancement Framework for Scene Graph Generation; Xiaoguang Chang et al
• TRANSFORMER-BASED IMAGE GENERATION FROM SCENE GRAPHS; Renato Sortino et al
• The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generation; Lin Li et al
• Knowledge-augmented Few-shot Visual Relation Detection; Tianyu Yu et al
• Prototype-based Embedding Network for Scene Graph Generation; Chaofan Zhen et al
• Unified Visual Relationship Detection with Vision and Language Models; Long Zhao et al
• Structure-CLIP: Enhance Multi-modal Language Representations with Structure Knowledge; Yufeng Huang et al

Attribute

• COCO Attributes: Attributes for People, Animals, and Objects; Genevieve Patterson et al
• Human Attribute Recognition by Deep Hierarchical Contexts; Yining Li et al; Attribute prediction in specific domains.
• Emotion Recognition in Context; Ronak Kosti et al; Attribute prediction in specific domains.
• The iMaterialist Fashion Attribute Dataset; Sheng Guo et al; Attribute prediction in specific domains.
• Learning to Predict Visual Attributes in the Wild; Khoi Pham et al
• Open-vocabulary Attribute Detection; Marıa A. Bravo et al
• OvarNet: Towards Open-vocabulary Object Attribute Recognition; Keyan Chen et al

Compositionality

• CREPE: Can Vision-Language Foundation Models Reason Compositionally?; Zixian Ma et al
• Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality; Tristan Thrush et al
• WHEN AND WHY VISION-LANGUAGE MODELS BEHAVE LIKE BAGS-OF-WORDS, AND WHAT TO DO ABOUT IT?; Mert Yuksekgonul et al
• GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering; Drew A. Hudson et al
• COVR: A Test-Bed for Visually Grounded Compositional Generalization with Real Images; Ben Bogin et al
• Cops-Ref: A new Dataset and Task on Compositional Referring Expression Comprehension; Zhenfang Chen et al
• Do Vision-Language Pretrained Models Learn Composable Primitive Concepts?; Tian Yun et al
• SUGARCREPE: Fixing Hackable Benchmarks for Vision-Language Compositionality; Cheng-Yu Hsieh et al
• An Examination of the Compositionality of Large Generative Vision-Language Models; Teli Ma et al

Concept

• Cross-Modal Concept Learning and Inference for Vision-Language Models; Yi Zhang et al
• Hierarchical Visual Primitive Experts for Compositional Zero-Shot Learning; Hanjae Kim et al

Planning

• Multimedia Generative Script Learning for Task Planning; Qingyun Wang et al; Next step prediction.
• PlaTe: Visually-Grounded Planning with Transformers in Procedural Tasks; Jiankai Sun et al; Procedure planning.
• P3IV: Probabilistic Procedure Planning from Instructional Videos with Weak Supervision; He Zhao et al; Procedure planning. Using text as weak supervision to replace video clips.
• Procedure Planning in Instructional Videos; Chien-Yi Chang et al; Procedure planning.
• ViLPAct: A Benchmark for Compositional Generalization on Multimodal Human Activities; Terry Yue Zhuo et al
• Actional Atomic-Concept Learning for Demystifying Vision-Language Navigation; Bingqian Lin et al

Reasoning

• VisualCOMET: Reasoning about the Dynamic Context of a Still Image; Jae Sung Park et al; Benchmark dataset, requiring models to reason about a still image (what happen past & next).
• Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering; Pan Lu et al
• See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning; Zhenfang Chen et al
• An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA; Zhengyuan Yang et al
• Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering; Pan Lu et al
• Multimodal Chain-of-Thought Reasoning in Language Models; Zhuosheng Zhang et al
• LAMPP: Language Models as Probabilistic Priors for Perception and Action; Belinda Z. Li et al
• Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings; Daniel Rose et al
• Symbol-LLM: Leverage Language Models for Symbolic System in Visual Human Activity Reasoning; Xiaoqian Wu et al

Common sense.

• Improving Commonsense in Vision-Language Models via Knowledge Graph Riddles; Shuquan Ye et al
• VIPHY: Probing “Visible” Physical Commonsense Knowledge; Shikhar Singh et al
• Visual Commonsense in Pretrained Unimodal and Multimodal Models; Chenyu Zhang et al

Generation

• ClipCap: CLIP Prefix for Image Captioning; Ron Mokady et al; Train an light-weight encoder to convert CLIP embeddings to prefix token embeddings of GPT-2.
• Multimodal Knowledge Alignment with Reinforcement Learning; Youngjae Yu et al; Use RL to train an encoder that projects multimodal inputs into the word embedding space of GPT-2.

Representation Learning

• Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering; Peter Anderson et al
• Fusion of Detected Objects in Text for Visual Question Answering; Chris Alberti et al
• VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix; Teng Wang et al
• Vision-Language Pre-Training with Triple Contrastive Learning; Jinyu Yang et al
• Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision; Hao Tan et al; Use visual supervision to pretrain language models.
• HighMMT: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation Learning; Paul Pu Liang et al
• Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture; Mahmoud Assran et al
• PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World; Rowan Zellers et al
• Learning the Effects of Physical Actions in a Multi-modal Environment; Gautier Dagan et al
• Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models; Zhiqiu Lin et al
• Learning Visual Representations via Language-Guided Sampling; Mohamed El Banani et al
• Image as Set of Points; Xu Ma et al
• ARCL: ENHANCING CONTRASTIVE LEARNING WITH AUGMENTATION-ROBUST REPRESENTATIONS; Xuyang Zhao et al
• BRIDGING THE GAP TO REAL-WORLD OBJECT-CENTRIC LEARNING; Maximilian Seitzer et al
• Learning Transferable Spatiotemporal Representations from Natural Script Knowledge; Ziyun Zeng et al
• Understanding and Constructing Latent Modality Structures in Multi-Modal Representation Learning; Qian Jiang et al

LLM Analysis

• GROKKING: GENERALIZATION BEYOND OVERFITTING ON SMALL ALGORITHMIC DATASETS; Alethea Power et al
• Unified View of Grokking, Double Descent and Emergent Abilities: A Perspective from Circuits Competition; Yufei Huang et al
• A Categorical Archive of ChatGPT Failures; Ali Borji et al
• Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling; Stella Biderman et al
• Are Emergent Abilities of Large Language Models a Mirage?; Rylan Schaeffer et al
• A Drop of Ink may Make a Million Think: The Spread of False Information in Large Language Models; Ning Bian et al
• Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting; Miles Turpin et al
• SYMBOL TUNING IMPROVES IN-CONTEXT LEARNING IN LANGUAGE MODELS; Jerry Wei et al
• What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task Learning; Jane Pan et al
• Measuring the Knowledge Acquisition-Utilization Gap in Pretrained Language Models; Amirhossein Kazemnejad et al
• Scaling Data-Constrained Language Models; Niklas Muennighoff et al
• The False Promise of Imitating Proprietary LLMs; Arnav Gudibande et al
• Counterfactual reasoning: Testing language models’ understanding of hypothetical scenarios; Jiaxuan Li et al
• Inverse Scaling: When Bigger Isn’t Better; Ian R. McKenzie et al
• DECODINGTRUST: A Comprehensive Assessment of Trustworthiness in GPT Models; Boxin Wang et al
• Lost in the Middle: How Language Models Use Long Contexts; Nelson F. Liu et al
• Won’t Get Fooled Again: Answering Questions with False Premises; Shengding Hu et al
• Generating Benchmarks for Factuality Evaluation of Language Models; Dor Muhlgay et al
• Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations; Yanda Chen et al
• Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation; Ruiyang Ren et al
• Large Language Models Struggle to Learn Long-Tail Knowledge; Nikhil Kandpal et al
• SCALING RELATIONSHIP ON LEARNING MATHEMATICAL REASONING WITH LARGE LANGUAGE MODELS; Zheng Yuan et al
• Multimodal Neurons in Pretrained Text-Only Transformers; Sarah Schwettmann et al
• SIMPLE SYNTHETIC DATA REDUCES SYCOPHANCY IN LARGE LANGUAGE MODELS; Jerry Wei et al
• Studying Large Language Model Generalization with Influence Functions; Roger Grosse et al
• Taken out of context: On measuring situational awareness in LLMs; Lukas Berglund et al
• OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMs; Patrick Haller et al
• Neurons in Large Language Models: Dead, N-gram, Positional; Elena Voita et al
• Are Emergent Abilities in Large Language Models just In-Context Learning?; Sheng Lu et al
• The Reversal Curse: LLMs trained on “A is B” fail to learn “B is A”; Lukas Berglund et al
• Language Modeling Is Compression; Grégoire Delétang et al
• FROM LANGUAGE MODELING TO INSTRUCTION FOLLOWING: UNDERSTANDING THE BEHAVIOR SHIFT IN LLMS AFTER INSTRUCTION TUNING; Xuansheng Wu et al
• RESOLVING KNOWLEDGE CONFLICTS IN LARGE LANGUAGE MODELS; Yike Wang et al
• LARGE LANGUAGE MODELS CANNOT SELF-CORRECT REASONING YET; Jie Huang et al
• ASK AGAIN, THEN FAIL: LARGE LANGUAGE MODELS’ VACILLATIONS IN JUDGEMENT; Qiming Xie et al
• FRESHLLMS: REFRESHING LARGE LANGUAGE MODELS WITH SEARCH ENGINE AUGMENTATION; Tu Vu et al
• Demystifying Embedding Spaces using Large Language Models; Guy Tennenholtz et al
• An Emulator for Fine-Tuning Large Language Models using Small Language Models; Eric Mitchell et al
• UNVEILING A CORE LINGUISTIC REGION IN LARGE LANGUAGE MODELS; Jun Zhao et al
• DETECTING PRETRAINING DATA FROM LARGE LANGUAGE MODELS; Weijia Shi et al
• BENCHMARKING AND IMPROVING GENERATOR-VALIDATOR CONSISTENCY OF LMS; Xiang Lisa Li et al
• Trusted Source Alignment in Large Language Models; Vasilisa Bashlovkina et al
• THE UNLOCKING SPELL ON BASE LLMS: RETHINKING ALIGNMENT VIA IN-CONTEXT LEARNING; Bill Yuchen Lin et al
• Can Large Language Models Really Improve by Self-critiquing Their Own Plans?; Karthik Valmeekam et al
• TELL, DON’T SHOW: DECLARATIVE FACTS INFLUENCE HOW LLMS GENERALIZE; Alexander Meinke et al
• A Closer Look at the Limitations of Instruction Tuning; Sreyan Ghosh et al
• PERSONAS AS A WAY TO MODEL TRUTHFULNESS IN LANGUAGE MODELS; Nitish Joshi et al
• Beyond Probabilities: Unveiling the Misalignment in Evaluating Large Language Models; Chenyang Lyu et al
• Dated Data: Tracing Knowledge Cutoffs in Large Language Models; Jeffrey Cheng et al
• Context versus Prior Knowledge in Language Models; Kevin Du et al
• Training Trajectories of Language Models Across Scales; Mengzhou Xia et al
• Retrieval Head Mechanistically Explains Long-Context Factuality; Wenhao Wu et al
• Let’s Think Dot by Dot: Hidden Computation in Transformer Language Models; Jacob Pfau et al
• Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?; Zorik Gekhman et al
• An Emulator for Fine-Tuning Large Language Models using Small Language Models; Eric Mitchell et al

Calibration & Uncertainty

• Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models; Alfonso Amayuelas et al
• Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs; Miao Xiong et al
• LLAMAS KNOW WHAT GPTS DON’T SHOW: SURROGATE MODELS FOR CONFIDENCE ESTIMATION; Vaishnavi Shrivastava et al
• Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models; Kaitlyn Zhou et al
• R-Tuning: Teaching Large Language Models to Refuse Unknown Questions; Hanning Zhang et al
• Relying on the Unreliable: The Impact of Language Models’ Reluctance to Express Uncertainty; Kaitlyn Zhou et al
• Prudent Silence or Foolish Babble? Examining Large Language Models’ Responses to the Unknown; Genglin Liu et al
• Benchmarking LLMs via Uncertainty Quantification; Fanghua Ye et al
• Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback; Katherine Tian et al
• Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Models; Anthony Sicilia et al
• Calibrating Long-form Generations from Large Language Models; Yukun Huang et al
• Distinguishing the Knowable from the Unknowable with Language Models; Gustaf Ahdritz et al
• Introspective Planning: Guiding Language-Enabled Agents to Refine Their Own Uncertainty; Kaiqu Liang et al
• Asking the Right Question at the Right Time: Human and Model Uncertainty Guidance to Ask Clarification Questions; Alberto Testoni et al
• Into the Unknown: Self-Learning Large Language Models; Teddy Ferdinan et al
• The Internal State of an LLM Knows When It’s Lying; Amos Azaria et al
• SELFCHECKGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models; Potsawee Manakul et al
• Calibrating Large Language Models with Sample Consistency; Qing Lyu et al
• Gotcha! Don’t trick me with unanswerable questions! Self-aligning Large Language Models for Responding to Unknown Questions; Yang Deng et al
• Unfamiliar Finetuning Examples Control How Language Models Hallucinate; Katie Kang et al
• Few-Shot Recalibration of Language Models; Xiang Lisa Li et al
• When to Trust LLMs: Aligning Confidence with Response Quality and Exploring Applications in RAG; Shuchang Tao et al
• Linguistic Calibration of Language Models; Neil Band et al

LLM Safety

• Learning Human Objectives by Evaluating Hypothetical Behavior; Siddharth Reddy et al
• Universal and Transferable Adversarial Attacks on Aligned Language Models; Andy Zou et al
• XSTEST: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models; Paul Röttger et al
• Jailbroken: How Does LLM Safety Training Fail? Content Warning: This paper contains examples of harmful language; Alexander Wei et al
• FUNDAMENTAL LIMITATIONS OF ALIGNMENT IN LARGE LANGUAGE MODELS; Yotam Wolf et al
• BEAVERTAILS: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset; Jiaming Ji et al
• GPT-4 IS TOO SMART TO BE SAFE: STEALTHY CHAT WITH LLMS VIA CIPHER; Youliang Yuan et al
• Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment; Rishabh Bhardwaj et al
• Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs; Yuxia Wang et al
• SafetyBench: Evaluating the Safety of Large Language Models with Multiple Choice Questions; Zhexin Zhang et al
• Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions; Federico Bianchi et al
• Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations; Hakan Inan et al
• EMULATED DISALIGNMENT: SAFETY ALIGNMENT FOR LARGE LANGUAGE MODELS MAY BACKFIRE!; Zhanhui Zhou et al
• Logits of API-Protected LLMs Leak Proprietary Information; Matthew Finlayson et al
• Simple probes can catch sleeper agents; Monte MacDiarmid et al
• AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs; Anselm Paulus et al

LLM Evaluation

• IS CHATGPT A GENERAL-PURPOSE NATURAL LANGUAGE PROCESSING TASK SOLVER?; Chengwei Qin et al
• AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models; Wanjun Zhong et al
• A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity; Yejin Bang et al
• On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective; Jindong Wang et al
• A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models; Junjie Ye et al
• KoLA: Carefully Benchmarking World Knowledge of Large Language Models; Jifan Yu et al
• SCIBENCH: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models; Xiaoxuan Wang et al
• FLASK: FINE-GRAINED LANGUAGE MODEL EVALUATION BASED ON ALIGNMENT SKILL SETS; Seonghyeon Ye et al
• Efficient Benchmarking (of Language Models); Yotam Perlitz et al
• Can Large Language Models Understand Real-World Complex Instructions?; Qianyu He et al
• NLPBENCH: EVALUATING LARGE LANGUAGE MODELS ON SOLVING NLP PROBLEMS; Linxin Song et al
• CALIBRATING LLM-BASED EVALUATOR; Yuxuan Liu et al
• GPT-FATHOM: BENCHMARKING LARGE LANGUAGE MODELS TO DECIPHER THE EVOLUTIONARY PATH TOWARDS GPT-4 AND BEYOND; Shen Zheng et al
• L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models; Ansong Ni et al
• Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations; Lifan Yuan et al
• TIGERSCORE: TOWARDS BUILDING EXPLAINABLE METRIC FOR ALL TEXT GENERATION TASKS; Dongfu Jiang et al
• DO LARGE LANGUAGE MODELS KNOW ABOUT FACTS?; Xuming Hu et al
• PROMETHEUS: INDUCING FINE-GRAINED EVALUATION CAPABILITY IN LANGUAGE MODELS; Seungone Kim et al
• CRITIQUE ABILITY OF LARGE LANGUAGE MODELS; Liangchen Luo et al
• BotChat: Evaluating LLMs’ Capabilities of Having Multi-Turn Dialogues; Haodong Duan et al
• Instruction-Following Evaluation for Large Language Models; Jeffrey Zhou et al
• GAIA: A Benchmark for General AI Assistants; Gregoire Mialon et al
• ML-BENCH: LARGE LANGUAGE MODELS LEVERAGE OPEN-SOURCE LIBRARIES FOR MACHINE LEARNING TASKS; Yuliang Liu et al
• TASKBENCH: BENCHMARKING LARGE LANGUAGE MODELS FOR TASK AUTOMATION; Yongliang Shen et al
• GENERATIVE JUDGE FOR EVALUATING ALIGNMENT; Junlong Li et al
• InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks; Xueyu Hu et al
• AGENTBOARD: AN ANALYTICAL EVALUATION BOARD OF MULTI-TURN LLM AGENTS; Chang Ma et al
• WEBLINX: Real-World Website Navigation with Multi-Turn Dialogue; Xing Han Lu et al
• MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization; Zhiyu Yang et al
• Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference; Wei-Lin Chiang et al
• DevBench: A Comprehensive Benchmark for Software Development; Bowen Li et al
• REWARDBENCH: Evaluating Reward Models for Language Modeling; Nathan Lambert et al
• Long-context LLMs Struggle with Long In-context Learning; Tianle Li et al
• LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models; Shibo Hao et al
• Benchmarking Benchmark Leakage in Large Language Models; Ruijie Xu et al
• PROMETHEUS 2: An Open Source Language Model Specialized in Evaluating Other Language Models; Seungone Kim et al
• Revealing the structure of language model capabilities; Ryan Burnell et al

LLM Reasoning

• STaR: Self-Taught Reasoner Bootstrapping Reasoning With Reasoning; Eric Zelikman et al
• Generated Knowledge Prompting for Commonsense Reasoning; Jiacheng Liu et al
• SELF-CONSISTENCY IMPROVES CHAIN OF THOUGHT REASONING IN LANGUAGE MODELS; Xuezhi Wang et al
• LEAST-TO-MOST PROMPTING ENABLES COMPLEX REASONING IN LARGE LANGUAGE MODELS; Denny Zhou et al
• REACT: SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS; Shunyu Yao et al
• The Capacity for Moral Self-Correction in Large Language Models; Deep Ganguli et al
• Learning to Reason and Memorize with Self-Notes; Jack lanchantin et al
• Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models; Lei Wang et al
• T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Large Language Model Signals for Science Question Answering; Lei Wang et al
• Tree of Thoughts: Deliberate Problem Solving with Large Language Models; Shunyu Yao et al
• Introspective Tips: Large Language Model for In-Context Decision Making; Liting Chen et al
• Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples; Abulhair Saparov et al
• Reasoning with Language Model is Planning with World Model; Shibo Hao et al
• Interpretable Math Word Problem Solution Generation Via Step-by-step Planning; Mengxue Zhang et al
• Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters; Boshi Wang et al
• Recursion of Thought: A Divide-and-Conquer Approach to Multi-Context Reasoning with Language Models; Soochan Lee et al
• Large Language Models Are Reasoning Teachers; Namgyu Ho et al
• Meta-Reasoning: Semantics-Symbol Deconstruction For Large Language Models; Yiming Wang et al
• BeamSearchQA: Large Language Models are Strong Zero-Shot QA Solver; Hao Sun et al
• AdaPlanner: Adaptive Planning from Feedback with Language Models; Haotian Sun et al
• ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models; Binfeng Xu et al
• SKILLS-IN-CONTEXT PROMPTING: UNLOCKING COMPOSITIONALITY IN LARGE LANGUAGE MODELS; Jiaao Chen et al
• SOLVING CHALLENGING MATH WORD PROBLEMS USING GPT-4 CODE INTERPRETER WITH CODE-BASED SELF-VERIFICATION; Aojun Zhou et al
• MAMMOTH: BUILDING MATH GENERALIST MODELS THROUGH HYBRID INSTRUCTION TUNING; Xiang Yue et al
• DESIGN OF CHAIN-OF-THOUGHT IN MATH PROBLEM SOLVING; Zhanming Jie et al
• NATURAL LANGUAGE EMBEDDED PROGRAMS FOR HYBRID LANGUAGE SYMBOLIC REASONING; Tianhua Zhang et al
• MATHCODER: SEAMLESS CODE INTEGRATION IN LLMS FOR ENHANCED MATHEMATICAL REASONING; Ke Wang et al
• META-COT: GENERALIZABLE CHAIN-OF-THOUGHT PROMPTING IN MIXED-TASK SCENARIOS WITH LARGE LANGUAGE MODELS; Anni Zou et al
• TOOLCHAIN: EFFICIENT ACTION SPACE NAVIGATION IN LARGE LANGUAGE MODELS WITH A SEARCH; Yuchen Zhuang et al
• Learning From Mistakes Makes LLM Better Reasoner; Shengnan An et al
• Chain of Code: Reasoning with a Language Model-Augmented Code Emulator; Chengshu Li et al
• Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives; Wenqi Zhang et al
• Divide and Conquer for Large Language Models Reasoning; Zijie Meng et al
• The Impact of Reasoning Step Length on Large Language Models; Mingyu Jin et al
• REFT: Reasoning with REinforced Fine-Tuning; Trung Quoc Luong et al
• Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding; Mirac Suzgun et al
• SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures; Pei Zhou et al
• Guiding Large Language Models with Divide-and-Conquer Program for Discerning Problem Solving; Yizhou Zhang et al
• Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning; Zhiheng Xi et al
• V-STaR: Training Verifiers for Self-Taught Reasoners; Arian Hosseini et al
• Verified Multi-Step Synthesis using Large Language Models and Monte Carlo Tree Search; David Brandfonbrener et al
• BOOSTING OF THOUGHTS: TRIAL-AND-ERROR PROBLEM SOLVING WITH LARGE LANGUAGE MODELS; Sijia Chen et al
• Language Agents as Optimizable Graphs; Mingchen Zhuge et al
• MathScale: Scaling Instruction Tuning for Mathematical Reasoning; Zhengyang Tang et al
• Teaching Large Language Models to Reason with Reinforcement Learning; Alex Havrilla et al
• Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking; Eric Zelikman et al

Self-consistency

• Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference; Eric Mitchell et al
• Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs; Angelica Chen et al
• Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation; Niels Mündler et al
• Measuring and Narrowing the Compositionality Gap in Language Models; Ofir Press et al
• Self-consistency for open-ended generations; Siddhartha Jain et al
• Question Decomposition Improves the Faithfulness of Model-Generated Reasoning; Ansh Radhakrishnan et al
• Measuring Faithfulness in Chain-of-Thought Reasoning; Tamera Lanham et al
• SELFCHECK: USING LLMS TO ZERO-SHOT CHECK THEIR OWN STEP-BY-STEP REASONING; Ning Miao et al
• On Measuring Faithfulness or Self-consistency of Natural Language Explanations; Letitia Parcalabescu et al
• Chain-of-Thought Unfaithfulness as Disguised Accuracy; Oliver Bentham et al

(with images)

• Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation; Arijit Ray et al
• Maintaining Reasoning Consistency in Compositional Visual Question Answering; Chenchen Jing et al
• SQuINTing at VQA Models: Introspecting VQA Models with Sub-Questions; Ramprasaath R. Selvaraju et al
• Logical Implications for Visual Question Answering Consistency; Sergio Tascon-Morales et al
• Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models; Adyasha Maharana et al
• Co-VQA: Answering by Interactive Sub Question Sequence; Ruonan Wang et al
• IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models; Haoxuan You et al
• Understanding ME? Multimodal Evaluation for Fine-grained Visual Commonsense; Zhecan Wang et al

LLM Application

• ArK: Augmented Reality with Knowledge Interactive Emergent Ability; Qiuyuan Huang et al
• Can Large Language Models Be an Alternative to Human Evaluation?; Cheng-Han Chiang et al
• Few-shot In-context Learning for Knowledge Base Question Answering; Tianle Li et al
• AutoML-GPT: Automatic Machine Learning with GPT; Shujian Zhang et al
• Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs; Jinyang Li et al
• Language models can explain neurons in language models; Steven Bills et al
• Large Language Model Programs; Imanol Schlag et al
• Evaluating Factual Consistency of Summaries with Large Language Models; Shiqi Chen et al
• WikiChat: A Few-Shot LLM-Based Chatbot Grounded with Wikipedia; Sina J. Semnani et al
• Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning; Xiaoming Shi et al
• Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks; Sherzod Hakimov et al
• PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents; Simeng Sun et al
• LayoutGPT: Compositional Visual Planning and Generation with Large Language Models; Weixi Feng et al
• Judging LLM-as-a-judge with MT-Bench and Chatbot Arena; Lianmin Zheng et al
• LLM-BLENDER: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion; Dongfu Jiang et al
• Benchmarking Foundation Models with Language-Model-as-an-Examiner; Yushi Bai et al
• AudioPaLM: A Large Language Model That Can Speak and Listen; Paul K. Rubenstein et al
• Human-in-the-Loop through Chain-of-Thought; Zefan Cai et al
• LARGE LANGUAGE MODELS ARE EFFECTIVE TEXT RANKERS WITH PAIRWISE RANKING PROMPTING; Zhen Qin et al
• Language to Rewards for Robotic Skill Synthesis; Wenhao Yu et al
• Visual Programming for Text-to-Image Generation and Evaluation; Jaemin Cho et al
• Mindstorms in Natural Language-Based Societies of Mind; Mingchen Zhuge et al
• Responsible Task Automation: Empowering Large Language Models as Responsible Task Automators; Zhizheng Zhang et al
• Large Language Models as General Pattern Machines; Suvir Mirchandani et al
• A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation; Neeraj Varshney et al
• VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models; Wenlong Huang et al
• External Reasoning: Towards Multi-Large-Language-Models Interchangeable Assistance with Human Feedback; Akide Liu et al
• OCTOPACK: INSTRUCTION TUNING CODE LARGE LANGUAGE MODELS; Niklas Muennighoff et al
• Tackling Vision Language Tasks Through Learning Inner Monologues; Diji Yang et al
• Can Language Models Learn to Listen?; Evonne Ng et al
• PROMPT2MODEL: Generating Deployable Models from Natural Language Instructions; Vijay Viswanathan et al
• AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models; Zhaopeng Gu et al
• LARGE LANGUAGE MODELS AS OPTIMIZERS; Chengrun Yang et al
• Large Language Model for Science: A Study on P vs. NP; Qingxiu Dong et al
• Physically Grounded Vision-Language Models for Robotic Manipulation; Jensen Gao et al
• Compositional Foundation Models for Hierarchical Planning; Anurag Ajay et al
• STRUC-BENCH: Are Large Language Models Really Good at Generating Complex Structured Data?; Xiangru Tang et al
• XATU: A Fine-grained Instruction-based Benchmark for Explainable Text Updates; Haopeng Zhang et al
• TEXT2REWARD: AUTOMATED DENSE REWARD FUNCTION GENERATION FOR REINFORCEMENT LEARNING; Tianbao Xie et al
• EUREKA: HUMAN-LEVEL REWARD DESIGN VIA CODING LARGE LANGUAGE MODELS; Yecheng Jason Ma et al
• CREATIVE ROBOT TOOL USE WITH LARGE LANGUAGE MODELS; Mengdi Xu et al
• Goal Driven Discovery of Distributional Differences via Language Descriptions; Ruiqi Zhong et al
• Can large language models provide useful feedback on research papers? A large-scale empirical analysis.; Weixin Liang et al
• DRIVEGPT4: INTERPRETABLE END-TO-END AUTONOMOUS DRIVING VIA LARGE LANGUAGE MODEL; Zhenhua Xu et al
• QUALEVAL: QUALITATIVE EVALUATION FOR MODEL IMPROVEMENT; Vishvak Murahari et al
• LLM AUGMENTED LLMS: EXPANDING CAPABILITIES THROUGH COMPOSITION; Rachit Bansal et al
• SpeechAgents: Human-Communication Simulation with Multi-Modal Multi-Agent Systems; Dong Zhang et al
• DEMOCRATIZING FINE-GRAINED VISUAL RECOGNITION WITH LARGE LANGUAGE MODELS; Mingxuan Liu et al
• Solving olympiad geometry without human demonstrations; Trieu H. Trinh et al
• AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy; Philipp Schoenegger et al
• What Evidence Do Language Models Find Convincing?; Alexander Wan et al

LLM with Memory

• Neural Turing Machines; Alex Graves et al
• Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study; Xiangyang Mou et al
• Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories; David Wilmot et al
• MemPrompt: Memory-assisted Prompt Editing with User Feedback; Aman Madaan et al
• LANGUAGE MODEL WITH PLUG-IN KNOWLEDGE MEMORY; Xin Cheng et al
• Assessing Working Memory Capacity of ChatGPT; Dongyu Gong et al
• Prompted LLMs as Chatbot Modules for Long Open-domain Conversation; Gibbeum Lee et al
• Beyond Goldfish Memory: Long-Term Open-Domain Conversation; Jing Xu et al
• Memory Augmented Large Language Models are Computationally Universal; Dale Schuurmans et al
• MemoryBank: Enhancing Large Language Models with Long-Term Memory; Wanjun Zhong et al
• Adaptive Chameleon or Stubborn Sloth: Unraveling the Behavior of Large Language Models in Knowledge Clashes; Jian Xie et al
• RET-LLM: Towards a General Read-Write Memory for Large Language Models; Ali Modarressi et al
• RECURRENTGPT: Interactive Generation of (Arbitrarily) Long Text; Wangchunshu Zhou et al
• MEMORIZING TRANSFORMERS; Yuhuai Wu et al
• Augmenting Language Models with Long-Term Memory; Weizhi Wang et al
• Statler: State-Maintaining Language Models for Embodied Reasoning; Takuma Yoneda et al
• LONGNET: Scaling Transformers to 1,000,000,000 Tokens; Jiayu Ding et al
• In-context Autoencoder for Context Compression in a Large Language Model; Tao Ge et al
• MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation; Junru Lu et al
• KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases; Xintao Wang et al
• LONGBENCH: A BILINGUAL, MULTITASK BENCHMARK FOR LONG CONTEXT UNDERSTANDING; Yushi Bai et al
• ChipNeMo: Domain-Adapted LLMs for Chip Design; Mingjie Liu et al
• LongAlign: A Recipe for Long Context Alignment of Large Language Models; Yushi Bai et al
• RAPTOR: RECURSIVE ABSTRACTIVE PROCESSING FOR TREE-ORGANIZED RETRIEVAL; Parth Sarthi et al
• UniMem: Towards a Unified View of Long-Context Large Language Models; Junjie Fang et al
• A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts; Kuang-Huei Lee et al
• Transformers Can Achieve Length Generalization But Not Robustly; Yongchao Zhou et al
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context; Gemini Team, Google

Retrieval-augmented LLM

• Training Language Models with Memory Augmentation; Zexuan Zhong et al
• Enabling Large Language Models to Generate Text with Citations; Tianyu Gao et al
• Multiview Identifiers Enhanced Generative Retrieval; Yongqi Li et al
• Meta-training with Demonstration Retrieval for Efficient Few-shot Learning; Aaron Mueller et al
• SELF-RAG: LEARNING TO RETRIEVE, GENERATE, AND CRITIQUE THROUGH SELF-REFLECTION; Akari Asai et ak
• RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents; Tomoyuki Kagaya et al

LLM with Human

• CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities; Mina Lee et al
• RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting; Lei Shu et al
• LeanDojo: Theorem Proving with Retrieval-Augmented Language Models; Kaiyu Yang et al
• Evaluating Human-Language Model Interaction; Mina Lee et al

LLM Foundation

• Retentive Network: A Successor to Transformer for Large Language Models; Yutao Sun et al
• Skill-it! A Data-Driven Skills Framework for Understanding and Training Language Models; Mayee F. Chen et al
• Secrets of RLHF in Large Language Models Part I: PPO; Rui Zheng et al
• EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education; Yuhao Dan et al
• WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct; Haipeng Luo et al
• SlimPajama-DC: Understanding Data Combinations for LLM Training; Zhiqiang Shen et al
• LMSYS-CHAT-1M: A LARGE-SCALE REAL-WORLD LLM CONVERSATION DATASET; Lianmin Zheng et al
• Mistral 7B; Albert Q. Jiang et al
• Tokenizer Choice For LLM Training: Negligible or Crucial?; Mehdi Ali et al
• ZEPHYR: DIRECT DISTILLATION OF LM ALIGNMENT; Lewis Tunstall et al
• LEMUR: HARMONIZING NATURAL LANGUAGE AND CODE FOR LANGUAGE AGENTS; Yiheng Xu et al
• System 2 Attention (is something you might need too); Jason Weston et al
• Camels in a Changing Climate: Enhancing LM Adaptation with TÜLU 2; Hamish Ivison et al
• The Falcon Series of Open Language Models; Ebtesam Almazrouei et al
• LLM360: Towards Fully Transparent Open-Source LLMs; Zhengzhong Liu et al
• OLMO: Accelerating the Science of Language Models; Dirk Groeneveld et al
• Executable Code Actions Elicit Better LLM Agents; Xingyao Wang et al
• InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning; Huaiyuan Ying et al
• Gemma: Open Models Based on Gemini Research and Technology; Gemma Team
• StarCoder 2 and The Stack v2: The Next Generation; Anton Lozhkov et al
• Yi: Open Foundation Models by 01.AI; 01.AI
• InternLM2 Technical Report; Zheng Cai et al
• Jamba: A Hybrid Transformer-Mamba Language Model; Opher Lieber et al
• JetMoE: Reaching Llama2 Performance with 0.1M Dollars; Yikang Shen et al
• RHO-1: Not All Tokens Are What You Need; Zhenghao Lin et al
• DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model; DeepSeek-AI

Scaling Law

• Scaling Laws for Neural Language Models; Jared Kaplan et al
• Explaining Neural Scaling Laws; Yasaman Bahri et al
• Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws; Zeyuan Allen-Zhu et al
• Training Compute-Optimal Large Language Models; Jordan Hoffmann et al
• Scaling Laws for Autoregressive Generative Modeling; Tom Henighan et al
• Scaling Laws for Generative Mixed-Modal Language Models; Armen Aghajanyan et al
• Beyond neural scaling laws: beating power law scaling via data pruning; Ben Sorscher et al
• Scaling Vision Transformers; Xiaohua Zhai et al
• Scaling Laws for Reward Model Overoptimization; Leo Gao et al
• Scaling Laws from the Data Manifold Dimension; Utkarsh Sharma et al
• BROKEN NEURAL SCALING LAWS; Ethan Caballero et al
• Scaling Laws for Transfer; Danny Hernandez et al
• Scaling Data-Constrained Language Models; Niklas Muennighoff et al
• Revisiting Neural Scaling Laws in Language and Vision; Ibrahim Alabdulmohsin et al
• SCALE EFFICIENTLY: INSIGHTS FROM PRE-TRAINING AND FINE-TUNING TRANSFORMERS; Yi Tay et al
• Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer; Greg Yang et al
• Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster; Nolan Dey et al
• Language models scale reliably with over-training and on downstream tasks; Samir Yitzhak Gadre et al
• Unraveling the Mystery of Scaling Laws: Part I; Hui Su et al
• nanoLM: an Affordable LLM Pre-training Benchmark via Accurate Loss Prediction across Scales; Yiqun Yao et al
• Understanding Emergent Abilities of Language Models from the Loss Perspective; Zhengxiao Du et al
• Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance; Jiasheng Ye et al
• The Fine Line: Navigating Large Language Model Pretraining with Down streaming Capability Analysis; Chen Yang et al
• UNLOCK PREDICTABLE SCALING FROM EMERGENT ABILITIES; Shengding Hu et al
• Scaling Laws for Data Filtering—Data Curation cannot be Compute Agnostic; Sachin Goyal et al
• A Large-Scale Exploration of µ-Transfer; Lucas Dax Lingle et al
• Observational Scaling Laws and the Predictability of Language Model Performance; Yangjun Ruan et al

MoE

• UNIFIED SCALING LAWS FOR ROUTED LANGUAGE MODELS; Aidan Clark et al
• SCALING LAWS FOR SPARSELY-CONNECTED FOUNDATION MODELS; Elias Frantar et al
• Toward Inference-optimal Mixture-of-Expert Large Language Models; Longfei Yun et al
• SCALING LAWS FOR FINE-GRAINED MIXTURE OF EXPERTS; Jakub Krajewski et al

LLM Data Engineering

• Textbooks Are All You Need II: phi-1.5 technical report; Yuanzhi Li et al
• Orca: Progressive Learning from Complex Explanation Traces of GPT-4; Subhabrata Mukherjee et al
• Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models; Fangzhi Xu et al
• Orca 2: Teaching Small Language Models How to Reason; Arindam Mitra et al
• REST MEETS REACT: SELF-IMPROVEMENT FOR MULTI-STEP REASONING LLM AGENT; Renat Aksitov et al
• WHAT MAKES GOOD DATA FOR ALIGNMENT? A COMPREHENSIVE STUDY OF AUTOMATIC DATA SELECTION IN INSTRUCTION TUNING; Wei Liu et al
• ChatQA: Building GPT-4 Level Conversational QA Models; Zihan Liu et al
• AGENTOHANA: DESIGN UNIFIED DATA AND TRAINING PIPELINE FOR EFFECTIVE AGENT LEARNING; Jianguo Zhang et al
• Advancing LLM Reasoning Generalists with Preference Trees; Lifan Yuan et al
• WILDCHAT: 1M CHATGPT INTERACTION LOGS IN THE WILD; Wenting Zhao et al
• MAmmoTH2: Scaling Instructions from the Web; Xiang Yue et al

VLM Data Engineering

• Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models; Lei Li et al
• MANTIS: Interleaved Multi-Image Instruction Tuning; Dongfu Jiang et al

Alignment

• AI Alignment Research Overview; Jacob Steinhardt
• Language Model Alignment with Elastic Reset; Michael Noukhovitch et al
• Alignment for Honesty; Yuqing Yang et al
• Align on the Fly: Adapting Chatbot Behavior to Established Norms; Chunpu Xu et al
• Combining weak-to-strong generalization with scalable oversight A high-level view on how this new approach fits into our alignment plans; JAN LEIKE
• SLEEPER AGENTS: TRAINING DECEPTIVE LLMS THAT PERSIST THROUGH SAFETY TRAINING; Evan Hubinger et al
• Towards Efficient and Exact Optimization of Language Model Alignment; Haozhe Ji et al
• Aligner: Achieving Efficient Alignment through Weak-to-Strong Correction; Jiaming Ji et al
• DeAL: Decoding-time Alignment for Large Language Models; James Y. Huang et al
• Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping; Haoyu Wang et al
• Dissecting Human and LLM Preferences; Junlong Li1 et al
• Reformatted Alignment; Run-Ze Fan et al
• Capability or Alignment? Respect the LLM Base Model’s Capability During Alignment; Jingfeng Yang
• Learning or Self-aligning? Rethinking Instruction Fine-tuning; Mengjie Ren et al
• Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models; Yi Luo et al

Scalable Oversight & SuperAlignment

• Supervising strong learners by amplifying weak experts; Paul Christiano et al
• Deep Reinforcement Learning from Human Preferences; Paul F Christiano et al
• AI safety via debate; Geoffrey Irving et al
• Scalable agent alignment via reward modeling: a research direction; Jan Leike et al
• Recursively Summarizing Books with Human Feedback; Jeff Wu et al
• Self-critiquing models for assisting human evaluators; William Saunders et al
• Measuring Progress on Scalable Oversight for Large Language Models; Samuel R. Bowman et al
• Debate Helps Supervise Unreliable Experts; Julian Michael et al
• WEAK-TO-STRONG GENERALIZATION: ELICITING STRONG CAPABILITIES WITH WEAK SUPERVISION; Collin Burns et al
• Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models; Zixiang Chen et al
• Discovering Language Model Behaviors with Model-Written Evaluations; Ethan Perez et al
• Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models; Hongzhan Lin et al
• Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate; Tian Liang et al
• Improving Weak-to-Strong Generalization with Scalable Oversight and Ensemble Learning; Jitao Sang et al
• PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations; Ruosen Li et al
• Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models; Jianyuan Guo et al
• Improving Factuality and Reasoning in Language Models through Multiagent Debate; Yilun Du et al
• CHATEVAL: TOWARDS BETTER LLM-BASED EVALUATORS THROUGH MULTI-AGENT DEBATE; Chi-Min Chan et al
• Debating with More Persuasive LLMs Leads to More Truthful Answers; Akbir Khan et al
• Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision; Zhiqing Sun et al

RL Foundation

• PREFERENCES IMPLICIT IN THE STATE OF THE WORLD; Rohin Shah et al
• Hindsight Experience Replay; Marcin Andrychowicz et al
• Learning to Reach Goals via Iterated Supervised Learning; Dibya Ghosh et al
• The Wisdom of Hindsight Makes Language Models Better Instruction Followers; Tianjun Zhang et al
• REWARD UNCERTAINTY FOR EXPLORATION IN PREFERENCE-BASED REINFORCEMENT LEARNING; Xinran Liang et al

Beyond Bandit

• ZERO-SHOT GOAL-DIRECTED DIALOGUE VIA RL ON IMAGINED CONVERSATIONS; Joey Hong et al
• LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models; Marwa Abdulhai et al
• ELICITING HUMAN PREFERENCES WITH LANGUAGE MODELS; Belinda Z. Li et al
• ITERATED DECOMPOSITION: IMPROVING SCIENCE Q&A BY SUPERVISING REASONING PROCESSES; Justin Reppert et al
• Let’s Verify Step by Step; Hunter Lightman et al
• Solving math word problems with process and outcome-based feedback; Jonathan Uesato et al
• EMPOWERING LANGUAGE MODELS WITH ACTIVE INQUIRY FOR DEEPER UNDERSTANDING A PREPRINT; Jing-Cheng Pang et al
• Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models; Zhiyuan Hu et al
• Tell Me More! Towards Implicit User Intention Understanding of Language Model-Driven Agents; Cheng Qian et al
• MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues; Ge Bai et al
• ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL; Yifei Zhou et al
• Probing the Multi-turn Planning Capabilities of LLMs via 20 Question Games; Yizhe Zhang et al
• STaR-GATE: Teaching Language Models to Ask Clarifying Questions; Chinmaya Andukuri et al
• Bayesian Preference Elicitation with Language Models; Kunal Handa et al
• Multi-turn Reinforcement Learning from Preference Human Feedback; Lior Shani et al

Agent

• Generative Agents: Interactive Simulacra of Human Behavior; Joon Sung Park et al
• SWIFTSAGE: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks; Bill Yuchen Lin et al
• Large Language Model Is Semi-Parametric Reinforcement Learning Agent; Danyang Zhang et al
• The Role of Summarization in Generative Agents: A Preliminary Perspective; Xiachong Feng et al
• CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society; Guohao Li et al
• Plan, Eliminate, and Track-Language Models are Good Teachers for Embodied Agents; Yue Wu et al
• Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents; Zihao Wang et al
• Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory; Xizhou Zhu et al
• TOWARDS A UNIFIED AGENT WITH FOUNDATION MODELS; Norman Di Palo et al
• MotionLM: Multi-Agent Motion Forecasting as Language Modeling; Ari Seff et al
• A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis; Izzeddin Gur et al
• Guide Your Agent with Adaptive Multimodal Rewards; Changyeon Kim et al
• Generative Agents: Interactive Simulacra of Human Behavior; Joon Sung Park et al
• AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents; Weize Chen et al
• METAGPT: META PROGRAMMING FOR MULTI-AGENT COLLABORATIVE FRAMEWORK; Sirui Hong et al
• YOU ONLY LOOK AT SCREENS: MULTIMODAL CHAIN-OF-ACTION AGENTS; Zhuosheng Zhang et al
• SELF: LANGUAGE-DRIVEN SELF-EVOLUTION FOR LARGE LANGUAGE MODEL; Jianqiao Lu et al
• Towards End-to-End Embodied Decision Making via Multi-modal Large Language Model: Explorations with GPT4-Vision and Beyond; Liang Chen et al
• A Zero-Shot Language Agent for Computer Control with Structured Reflection; Tao Li et al
• Character-LLM: A Trainable Agent for Role-Playing; Yunfan Shao et al
• CLIN: A CONTINUALLY LEARNING LANGUAGE AGENT FOR RAPID TASK ADAPTATION AND GENERALIZATION; Bodhisattwa Prasad Majumder et al
• FIREACT: TOWARD LANGUAGE AGENT FINE-TUNING; Baian Chen et al
• TrainerAgent: Customizable and Efficient Model Training through LLM-Powered Multi-Agent System; Haoyuan Li et al
• LUMOS: LEARNING AGENTS WITH UNIFIED DATA, MODULAR DESIGN, AND OPEN-SOURCE LLMS; Da Yin et al
• TaskWeaver: A Code-First Agent Framework; Bo Qiao et al
• Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning; Filippos Christianos et al
• AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation; Qingyun Wu et al
• TRUE KNOWLEDGE COMES FROM PRACTICE: ALIGNING LLMS WITH EMBODIED ENVIRONMENTS VIA REINFORCEMENT LEARNING; Weihao Tan et al
• Investigate-Consolidate-Exploit: A General Strategy for Inter-Task Agent Self-Evolution; Cheng Qian et al
• OS-COPILOT: TOWARDS GENERALIST COMPUTER AGENTS WITH SELF-IMPROVEMENT; Zhiyong Wu et al
• LONGAGENT: Scaling Language Models to 128k Context through Multi-Agent Collaboration; Jun Zhao et al
• When is Tree Search Useful for LLM Planning? It Depends on the Discriminator; Ziru Chen et al
• DATA INTERPRETER: AN LLM AGENT FOR DATA SCIENCE; Sirui Hong et al
• Towards General Computer Control: A Multimodal Agent for Red Dead Redemption II as a Case Study; Weihao Tan et al
• SOTOPIA-π: Interactive Learning of Socially Intelligent Language Agents; Ruiyi Wang et al
• Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models; Zehui Chen et al
• LLM Agent Operating System; Kai Mei et al

AutoTelic Agent

• AUGMENTING AUTOTELIC AGENTS WITH LARGE LANGUAGE MODELS; Cedric Colas et al
• Visual Reinforcement Learning with Imagined Goals; Ashvin Nair et al

Evaluation

• AgentBench: Evaluating LLMs as Agents; Xiao Liu et al
• EVALUATING MULTI-AGENT COORDINATION ABILITIES IN LARGE LANGUAGE MODELS; Saaket Agashe et al
• OpenAgents: AN OPEN PLATFORM FOR LANGUAGE AGENTS IN THE WILD; Tianbao Xie et al
• SMARTPLAY : A BENCHMARK FOR LLMS AS INTELLIGENT AGENTS; Yue Wu et al
• WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?; Alexandre Drouin et al
• Autonomous Evaluation and Refinement of Digital Agents; Jiayi Pan et al

VL Related Task

• LANGNAV: LANGUAGE AS A PERCEPTUAL REPRESENTATION FOR NAVIGATION; Bowen Pan et al
• VIDEO LANGUAGE PLANNING; Yilun Du et al
• Fuyu-8B: A Multimodal Architecture for AI Agents; Rohan Bavishi et al
• GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigation; An Yan et al
• Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld; Yijun Yang et al
• STEVE: See and Think: Embodied Agent in Virtual Environment; Zhonghan Zhao et al
• JARVIS-1: Open-world Multi-task Agents with Memory-Augmented Multimodal Language Models; Zihao Wang et al
• STEVE-EYE: EQUIPPING LLM-BASED EMBODIED AGENTS WITH VISUAL PERCEPTION IN OPEN WORLDS; Sipeng Zheng et al
• OCTOPUS: EMBODIED VISION-LANGUAGE PROGRAMMER FROM ENVIRONMENTAL FEEDBACK; Jingkang Yang et al
• CogAgent: A Visual Language Model for GUI Agents; Wenyi Hong et al
• GPT-4V(ision) is a Generalist Web Agent, if Grounded; Boyuan Zheng et al
• WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models; Hongliang He et al
• MOBILE-AGENT: AUTONOMOUS MULTI-MODAL MOBILE DEVICE AGENT WITH VISUAL PERCEPTION; Junyang Wang et al
• V-IRL: Grounding Virtual Intelligence in Real Life; Jihan Yang et al
• An Interactive Agent Foundation Model; Zane Durante et al
• RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis; Yao Mu et al
• Scaling Instructable Agents Across Many Simulated Worlds; SIMA Team
• Do We Really Need a Complex Agent System? Distill Embod�ied Agent into a Single Model; Zhonghan Zhao1 et al
• Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs; Keen You et al
• OSWORLD: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments; Tianbao Xie et al

Interaction

• Imitating Interactive Intelligence; Interactive Agents Group
• Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning; Interactive Agents Team
• Evaluating Multimodal Interactive Agents; Interactive Agents Team
• Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback; Interactive Agents Team
• LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzles; Shulin Huang et al
• BENCHMARKING LARGE LANGUAGE MODELS AS AI RESEARCH AGENTS; Qian Huang et al
• ADAPTING LLM AGENTS THROUGH COMMUNICATION; Kuan Wang et al
• PARROT: ENHANCING MULTI-TURN CHAT MODELS BY LEARNING TO ASK QUESTIONS; Yuchong Sun et al
• LLAMA RIDER: SPURRING LARGE LANGUAGE MODELS TO EXPLORE THE OPEN WORLD; Yicheng Feng et al
• AGENTTUNING: ENABLING GENERALIZED AGENT ABILITIES FOR LLMS; Aohan Zeng et al
• MINT: Evaluating LLMs in Multi-Turn Interaction with Tools and Language Feedback; Xingyao Wang et al
• LLF-Bench: Benchmark for Interactive Learning from Language Feedback; Ching-An Cheng et al
• MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models; Wai-Chung Kwan et al
• Can large language models explore in-context?; Akshay Krishnamurthy et al
• LARGE LANGUAGE MODELS CAN INFER PERSONALITY FROM FREE-FORM USER INTERACTIONS; Heinrich Peters et al

Critique Modeling

• Learning Evaluation Models from Large Language Models for Sequence Generation; Chenglong Wang et al
• RETROFORMER: RETROSPECTIVE LARGE LANGUAGE AGENTS WITH POLICY GRADIENT OPTIMIZATION; Weiran Yao et al
• Shepherd: A Critic for Language Model Generation; Tianlu Wang et al
• GENERATING SEQUENCES BY LEARNING TO [SELF-]CORRECT; Sean Welleck et al
• ZYN: Zero-Shot Reward Models with Yes-No Questions; Victor Gallego et al
• LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked; Alec Helbling et al
• RAIN: Your Language Models Can Align Themselves Yuhui Liwithout Finetuning; Yuhui Li et al
• SYNDICOM: Improving Conversational Commonsense with Error-Injection and Natural Language Feedback; Christopher Richardson et al
• LET’S REWARD STEP BY STEP: STEP-LEVEL REWARD MODEL AS THE NAVIGATORS FOR REASONING; Qianli Ma et al
• MAF: Multi-Aspect Feedback for Improving Reasoning in Large Language Models; Deepak Nathani et al
• DON’T THROW AWAY YOUR VALUE MODEL! MAKING PPO EVEN BETTER VIA VALUE-GUIDED MONTE-CARLO TREE SEARCH DECODING; Jiacheng Liu et al
• COFFEE: Boost Your Code LLMs by Fixing Bugs with Feedback; Seungjun Moon et al
• Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer; Bowen Tan et al
• Pinpoint, Not Criticize: Refining Large Language Models via Fine-Grained Actionable Feedback; Wenda Xu et al
• Digital Socrates: Evaluating LLMs through explanation critiques; Yuling Gu et al
• Outcome-supervised Verifiers for Planning in Mathematical Reasoning; Fei Yu et al
• Data-Efficient Alignment of Large Language Models with Human Feedback Through Natural Language; Di Jin et al
• CRITIQUELLM: Scaling LLM-as-Critic for Effective and Explainable Evaluation of Large Language Model Generation; Pei Ke et al
• Mismatch Quest: Visual and Textual Feedback for Image-Text Misalignment; Brian Gordon et al
• MATH-SHEPHERD: A LABEL-FREE STEP-BY-STEP VERIFIER FOR LLMS IN MATHEMATICAL REASONING; Peiyi Wang et al
• The Critique of Critique; Shichao Sun et al
• LLMCRIT: Teaching Large Language Models to Use Criteria; Weizhe Yuan et al
• Multi-Level Feedback Generation with Large Language Models for Empowering Novice Peer Counselors; Alicja Chaszczewicz et al

MoE/Specialized

• OUTRAGEOUSLY LARGE NEURAL NETWORKS: THE SPARSELY-GATED MIXTURE-OF-EXPERTS LAYER; Noam Shazeer et al
• Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity; William Fedus et al
• DEMIX Layers: Disentangling Domains for Modular Language Modeling; Suchin Gururangan et al
• ModuleFormer: Learning Modular Large Language Models From Uncurated Data; Yikang Shen et al
• Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for Large Language Models; Sheng Shen et al
• From Sparse to Soft Mixtures of Experts; Joan Puigcerver et al
• SELF-SPECIALIZATION: UNCOVERING LATENT EXPERTISE WITHIN LARGE LANGUAGE MODELS; Junmo Kang et al
• HOW ABILITIES IN LARGE LANGUAGE MODELS ARE AFFECTED BY SUPERVISED FINE-TUNING DATA COMPOSITION; Guanting Dong et al
• OPENWEBMATH: AN OPEN DATASET OF HIGH-QUALITY MATHEMATICAL WEB TEXT; Keiran Paster et al
• LLEMMA: AN OPEN LANGUAGE MODEL FOR MATHEMATICS; Zhangir Azerbayev et al
• Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models; Keming Lu et al
• Mixtral of Experts; Albert Q. Jiang et al
• DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models; Damai Dai et al
• MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts; Maciej Pioro et al
• OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models; Fuzhao Xue et al
• Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM; Sainbayar Sukhbaatar et al
• Multi-Head Mixture-of-Experts; Xun Wu et al
• X FT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts; Yifeng Ding et al

Vision-Language Foundation Model

First Generation: Using region-based features; can be classified as one- and two- streams model architectures; Before 2020.6;

• Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs; Emanuele Bugliarello et al; A meta-analysis of the first generation VL models and a unified framework.
• Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers; Lisa Anne Hendricks et al
• ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks; Jiasen Lu et al
• LXMERT: Learning Cross-Modality Encoder Representations from Transformers; Hao Tan et al
• VISUALBERT: A SIMPLE AND PERFORMANT BASELINE FOR VISION AND LANGUAGE; Liunian Harold Li et al
• UNITER: UNiversal Image-TExt Representation Learning; Yen-Chun Chen et al
• VL-BERT: PRE-TRAINING OF GENERIC VISUAL-LINGUISTIC REPRESENTATIONS; Weijie Su et al
• IMAGEBERT: CROSS-MODAL PRE-TRAINING WITH LARGE-SCALE WEAK-SUPERVISED IMAGE-TEXT DATA; Di Qi et al
• Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training; Gen Li et al
• UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning; Wei Li et al; Motivate to use unimodal data to improve the performance of VL tasks.

Introduce image tags to learn image-text alignments.

• Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks; Xiujun Li et al
• VinVL: Revisiting Visual Representations in Vision-Language Models; Pengchuan Zhang et al
• Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions; Liunian Harold Li et al; Consider the unsupervised setting.
• Tag2Text: Guiding Vision-Language Model via Image Tagging; Xinyu Huang et al

Second Generation: Get rid of ROI and object detectors for acceleration; Moving to large pretraining datasets; Moving to unified architectures for understanding and generation tasks; Mostly before 2022.6.

• An Empirical Study of Training End-to-End Vision-and-Language Transformers; Zi-Yi Dou et al; Meta-analysis. Investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
• Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers; Zhicheng Huang et al; Throw away region-based features, bounding boxes, and object detectors. Directly input the raw pixels and use CNN to extract features.
• ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision; Wonjae Kim et al; Get rid of heavy computation of ROI and CNN through utilizing ViT.
• Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning; Zhicheng Huang et al
• E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning; Haiyang Xu et al; Get rid of bounding boxes; Introduce object detection and image captioning as pretraining tasks with a encoder-decoder structure.
• Align before Fuse: Vision and Language Representation Learning with Momentum Distillation; Junnan Li et al; Propose ALBEF.
• simvlm: simple visual language model pre-training with weak supervision; Zirui Wang et al; Get rid of bounding boxes; Further argue that the pretraining objectives are complicated and not scalable; Consider the zero-shot behaviors, emergent by pretraining on large datasets.
• UFO: A UniFied TransfOrmer for Vision-Language Representation Learning; Jianfeng Wang et al
• VLMO: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts; Hangbo Bao et al; Introduce the mixture-of-experts method to model text and image separately and use a specific expert to learn the cross-modal fusion (Multiway Transformer), which is later adopted by BEiT-3; Ensure better image-text retrieval (performance & speed) and VL tasks;
• Learning Transferable Visual Models From Natural Language Supervision; Alec Radford et al; Using large noisy pretraining datasets.
• Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision; Chao Jia et al; Using large noisy pretraining datasets.
• FILIP: FINE-GRAINED INTERACTIVE LANGUAGE-IMAGE PRE-TRAINING; Lewei Yao et al; Further improve CLIP & ALIGN by introducing fine-grained alignments.
• PERCEIVER IO: A GENERAL ARCHITECTURE FOR STRUCTURED INPUTS & OUTPUTS; Andrew Jaegle et al
• X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages; Feilong Chen et al

Special designs tailored to enhance the position encoding & grounding.

• UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling; Zhengyuan Yang et al
• PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models; Yuan Yao et al; Introduce explicit object position modeling.A woman < 310 mask 406 475 > is watching the mask < 175 86 254 460 >;
• GLIPv2: Unifying Localization and VL Understanding; Haotian Zhang et al; Further show that GLIP's pretraining method can benefit the VL task (Unifying localization and understanding).
• DesCo: Learning Object Recognition with Rich Language Descriptions; Liunian Harold Li et al

Motivate to use unparalleled image & text data to build a unified model for VL, vision, and language tasks and potentially bring better performance.

• Uni-Perceiver: Pre-training Unified Architecture for Generic Perception for Zero-shot and Few-shot Tasks; Xizhou Zhu et al; Siamese network to encode various modalities.
• FLAVA: A Foundational Language And Vision Alignment Model; Amanpreet Singh et al; A unified backbone model (need task-specific heads) for NLP, CV, and VL tasks.
• UNIMO-2: End-to-End Unified Vision-Language Grounded Learning; Wei Li et al; Design a new method "Grounded Dictionary Learning", similar to the sense of "continuous" image tags to align two modalities.

Third Generation: Chasing for one unified/general/generalist model to include more VL/NLP/CV tasks; Becoming larger & Stronger; 2022->Now.

• BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation; Junnan Li et al; New unified architecture and new method to generate and then filter captions.
• OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework; Peng Wang et al; A unified model (framework) to handle text, image, and image-text tasks.
• Webly Supervised Concept Expansion for General Purpose Vision Models; Amita Kamath et al
• Language Models are General-Purpose Interfaces; Yaru Hao et al
• GIT: A Generative Image-to-text Transformer for Vision and Language; Jianfeng Wang et al
• CoCa: Contrastive Captioners are Image-Text Foundation Models; Jiahui Yu et al
• Flamingo: a Visual Language Model for Few-Shot Learning; Jean-Baptiste Alayrac et al; Designed for few-shot learning.
• Image as a Foreign Language: BEIT Pretraining for All Vision and Vision-Language Tasks; Wenhui Wang et al; BEIT-3.
• OmniVL: One Foundation Model for Image-Language and Video-Language Tasks; Junke Wang et al; Support both image-language and video-language tasks and show the positive transfer in three modalities.
• Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks; Hao Li et al; Propose a generalist model that can also handle object detection and instance segmentation tasks.
• X2-VLM: All-In-One Pre-trained Model For Vision-Language Tasks; Yan Zeng et al; Propose a unified model for image-language and video-text-language tasks; Modeling the fine-grained alignments between image regions and descriptions.
• Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks; Xinsong Zhang et al
• mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video; Haiyang Xu et al
• KOSMOS-2: Grounding Multimodal Large Language Models to the World; Zhiliang Peng et al
• PaLI-X: On Scaling up a Multilingual Vision and Language Model; Xi Chen et al
• UNIFIED LANGUAGE-VISION PRETRAINING WITH DYNAMIC DISCRETE VISUAL TOKENIZATION; Yang Jin et al
• PALI-3 VISION LANGUAGE MODELS: SMALLER, FASTER, STRONGER; Xi Chen et al

Generalist models

• UNIFIED-IO: A UNIFIED MODEL FOR VISION, LANGUAGE, AND MULTI-MODAL TASKS; Jiasen Lu et al; Examine whether a single unified model can solve a variety of tasks (NLP, CV, VL) simultaneously; Construct a massive multi-tasking dataset by ensembling 95 datasets from 62 publicly available data sources, including Image Synthesis, Keypoint Estimation, Depth Estimation, Object Segmentation, et al; Focusing on multi-task fine-tuning.
• Generalized Decoding for Pixel, Image, and Language; Xueyan Zou et al
• Foundation Transformers; Hongyu Wang et al; Propose a new unified architecture.
• A Generalist Agent; Scott Reed et al
• PaLM-E: An Embodied Multimodal Language Model; Danny Driess et al
• IMAGEBIND: One Embedding Space To Bind Them All; Rohit Girdhar et al

Fourth Generation: Relying on LLMs and instruction tuning

• BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models; Junnan Li et al
• Grounding Language Models to Images for Multimodal Inputs and Outputs; Jing Yu Koh et al
• Language Is Not All You Need: Aligning Perception with Language Models; Shaohan Huang et al
• Otter: A Multi-Modal Model with In-Context Instruction Tuning; Bo Li et al
• Visual Instruction Tuning; Haotian Liu et al
• MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models; Deyao Zhu et al
• InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning; Wenliang Dai et al
• LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model; Peng Gao et al
• LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding; Yanzhe Zhang et al
• MultiModal-GPT: A Vision and Language Model for Dialogue with Humans; Tao Gong et al
• GPT-4 Technical Report; OpenAI
• mPLUG-Owl : Modularization Empowers Large Language Models with Multimodality; Qinghao Ye et al
• VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks; Wenhai Wang et al
• PandaGPT: One Model To Instruction-Follow Them All; Yixuan Su et al
• Generating Images with Multimodal Language Models; Jing Yu Koh et al
• What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?; Yan Zeng et al
• GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest; Shilong Zhang et al
• Generative Pretraining in Multimodality; Quan Sun et al
• Planting a SEED of Vision in Large Language Model; Yuying Ge et al
• ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning; Liang Zhao et al
• Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning; Lili Yu et al
• The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World; Weiyun Wang et al
• EMPOWERING VISION-LANGUAGE MODELS TO FOLLOW INTERLEAVED VISION-LANGUAGE INSTRUCTIONS; Juncheng Li et al
• RegionBLIP: A Unified Multi-modal Pre-training Framework for Holistic and Regional Comprehension; Qiang Zhou et al
• LISA: REASONING SEGMENTATION VIA LARGE LANGUAGE MODEL; Xin Lai et al
• Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities; Jinze Bai et al
• InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4; Lai Wei et al
• StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data; Yanda Li et al
• Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages; Jinyi Hu et al
• MMICL: EMPOWERING VISION-LANGUAGE MODEL WITH MULTI-MODAL IN-CONTEXT LEARNING; Haozhe Zhao et al
• An Empirical Study of Scaling Instruction-Tuned Large Multimodal Models; Yadong Lu et al
• ALIGNING LARGE MULTIMODAL MODELS WITH FACTUALLY AUGMENTED RLHF; Zhiqing Sun et al
• Reformulating Vision-Language Foundation Models and Datasets Towards Universal Multimodal Assistants; Tianyu Yu et al
• AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model; Seungwhan Moon et al
• InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition; Pan Zhang et al
• DREAMLLM: SYNERGISTIC MULTIMODAL COMPREHENSION AND CREATION; Runpei Dong et al
• HALLE-SWITCH: RETHINKING AND CONTROLLING OBJECT EXISTENCE HALLUCINATIONS IN LARGE VISION LANGUAGE MODELS FOR DETAILED CAPTION; Bohan Zhai et al
• Improved Baselines with Visual Instruction Tuning; Haotian Liu et al
• Fuyu-8B: A Multimodal Architecture for AI Agents; Rohan Bavishi et al
• MINIGPT-5: INTERLEAVED VISION-AND-LANGUAGE GENERATION VIA GENERATIVE VOKENS; Kaizhi Zheng et al
• Making LLaMA SEE and Draw with SEED Tokenizer; Yuying Ge et al
• To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning; Junke Wang et al
• TEAL: TOKENIZE AND EMBED ALL FOR MULTI-MODAL LARGE LANGUAGE MODELS; Zhen Yang et al
• mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration; Qinghao Ye et al
• LION: Empowering Multimodal Large Language Model with Dual-Level Visual Knowledge; Gongwei Chen et al
• OtterHD: A High-Resolution Multi-modality Model; Bo Li et al
• ShareGPT4V: Improving Large Multi-Modal Models with Better Captions; Lin Chen et al
• PerceptionGPT: Effectively Fusing Visual Perception into LLM; Renjie Pi et al
• OCTAVIUS: MITIGATING TASK INTERFERENCE IN MLLMS VIA MOE; Zeren Chen et al
• COGVLM: VISUAL EXPERT FOR LARGE LANGUAGE MODELS; Weihan Wang et al
• Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Models; Zhang Li et al
• Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts; Jialin Wu et al
• SILKIE: PREFERENCE DISTILLATION FOR LARGE VISUAL LANGUAGE MODELS; Lei Li et al
• GLaMM: Pixel Grounding Large Multimodal Model; Hanoona Rasheed et al
• TEXTBIND: MULTI-TURN INTERLEAVED MULTIMODAL INSTRUCTION-FOLLOWING IN THE WILD; Huayang Li et al
• DRESS: Instructing Large Vision-Language Models to Align and Interact with Humans via Natural Language Feedback; Yangyi Chen et al
• Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding; Peng Jin et al
• LLaVA-Grounding: Grounded Visual Chat with Large Multimodal Models; Hao Zhang et al
• mPLUG-PaperOwl: Scientific Diagram Analysis with the Multimodal Large Language Model; Anwen Hu et al
• Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models; Haoning Wu et al
• SPHINX: THE JOINT MIXING OF WEIGHTS, TASKS, AND VISUAL EMBEDDINGS FOR MULTI-MODAL LARGE LANGUAGE MODELS; Ziyi Lin et al
• DeepSpeed-VisualChat: Multi Round Multi Images Interleave Chat via Multi-Modal Casual Attention; Zhewei Yao et al
• Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models; Haoran Wei et al
• Osprey: Pixel Understanding with Visual Instruction Tuning; Yuqian Yuan et al
• Generative Multimodal Models are In-Context Learners; Quan Sun et al
• Gemini: A Family of Highly Capable Multimodal Models; Gemini Team
• CaMML: Context-Aware Multimodal Learner for Large Models; Yixin Chen et al
• MM-Interleaved: Interleaved Image-Text Generative Modeling via Multi-modal Feature Synchronizer; Changyao Tian et al
• InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Models; Xiaoyi Dong et al
• MoE-LLaVA: Mixture of Experts for Large Vision-Language Models; Bin Lin et al
• MouSi: Poly-Visual-Expert Vision-Language Models; Xiaoran Fan et al
• SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models; Peng Gao et al
• Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal Large Language Models; Gen Luo et al
• DeepSeek-VL: Towards Real-World Vision-Language Understanding; Haoyu Lu et al
• UniCode: Learning a Unified Codebook for Multimodal Large Language Models; Sipeng Zheng et al
• MoAI: Mixture of All Intelligence for Large Language and Vision Models; Byung-Kwan Lee et al
• LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images; Ruyi Xu et al
• Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models; Yanwei Li et al
• InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD; Xiaoyi Dong et al
• Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models; Haotian Zhang et al
• Self-Supervised Visual Preference Alignment; Ke Zhu et al
• How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites; Zhe Chen et al
• CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts; Jiachen Li et al
• Libra: Building Decoupled Vision System on Large Language Models; Yifan Xu et al
• Chameleon: Mixed-Modal Early-Fusion Foundation Models; Chameleon Team

Others

• Unified Vision-Language Pre-Training for Image Captioning and VQA; Luowei Zhou et al
• Unifying Vision-and-Language Tasks via Text Generation; Jaemin Cho et al
• MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound; Rowan Zellers et al
• CLIP-Event: Connecting Text and Images with Event Structures; Manling Li et al; The new model CLIP-Event, specifically designed for multi-modal event extraction. Introducing new pretraining tasks to enable strong zero-shot performances. From object-centric representations to event-centric representations.
• Scaling Vision-Language Models with Sparse Mixture of Experts; Sheng Shen et al
• MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks; Weicheng Kuo et al
• Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning; Zhiyang Xu et al

Vision-Language Model Application

• VISION-LANGUAGE FOUNDATION MODELS AS EFFECTIVE ROBOT IMITATORS; Xinghang Li et al
• LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing; Wei-Ge Chen et al
• Vision-Language Models as a Source of Rewards; Kate Baumli et al
• SELF-IMAGINE: EFFECTIVE UNIMODAL REASONING WITH MULTIMODAL MODELS USING SELF-IMAGINATION; Syeda Nahida Akter et al
• Code as Reward: Empowering Reinforcement Learning with VLMs; David Venuto et al
• MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark; Dongping Chen et al
• PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs; Soroush Nasiriany et al

Vision-Language Model Analysis & Evaluation

• What Makes for Good Visual Tokenizers for Large Language Models?; Guangzhi Wang et al
• LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models; Peng Xu et al
• MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models; Chaoyou Fu et al
• JourneyDB: A Benchmark for Generative Image Understanding; Junting Pan et al
• MMBench: Is Your Multi-modal Model an All-around Player?; Yuan Liu et al
• SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension; Bohao Li et al
• Tiny LVLM-eHub: Early Multimodal Experiments with Bard; Wenqi Shao et al
• MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities; Weihao Yu et al
• VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use; Yonatan Bitton et al
• TouchStone: Evaluating Vision-Language Models by Language Models; Shuai Bai et al
• Investigating the Catastrophic Forgetting in Multimodal Large Language Models; Yuexiang Zhai et al
• DEMYSTIFYING CLIP DATA; Hu Xu et al
• Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models; Yangyi Chen et al
• REFORM-EVAL: EVALUATING LARGE VISION LANGUAGE MODELS VIA UNIFIED RE-FORMULATION OF TASK-ORIENTED BENCHMARKS; Zejun Li1 et al
• REVO-LION: EVALUATING AND REFINING VISION LANGUAGE INSTRUCTION TUNING DATASETS; Ning Liao et al
• BEYOND TASK PERFORMANCE: EVALUATING AND REDUCING THE FLAWS OF LARGE MULTIMODAL MODELS WITH IN-CONTEXT LEARNING; Mustafa Shukor et al
• Grounded Intuition of GPT-Vision’s Abilities with Scientific Images; Alyssa Hwang et al
• Holistic Evaluation of Text-to-Image Models; Tony Lee et al
• CORE-MM: COMPLEX OPEN-ENDED REASONING EVALUATION FOR MULTI-MODAL LARGE LANGUAGE MODELS; Xiaotian Han et al
• HALLUSIONBENCH: An Advanced Diagnostic Suite for Entangled Language Hallucination & Visual Illusion in Large Vision-Language Models; Tianrui Guan et al
• SEED-Bench-2: Benchmarking Multimodal Large Language Models; Bohao Li et al
• MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI; Xiang Yue et al
• MATHVISTA: EVALUATING MATH REASONING IN VISUAL CONTEXTS WITH GPT-4V, BARD, AND OTHER LARGE MULTIMODAL MODELS; Pan Lu et al
• VILA: On Pre-training for Visual Language Models; Ji Lin et al
• TUNING LAYERNORM IN ATTENTION: TOWARDS EFFICIENT MULTI-MODAL LLM FINETUNING; Bingchen Zhao et al
• Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs; Shengbang Tong et al
• FROZEN TRANSFORMERS IN LANGUAGE MODELS ARE EFFECTIVE VISUAL ENCODER LAYERS; Ziqi Pang et al
• Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models; Siddharth Karamcheti et al
• Design2Code: How Far Are We From Automating Front-End Engineering?; Chenglei Si et al
• MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training; Brandon McKinzie et al
• MATHVERSE: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?; Renrui Zhang et al
• Are We on the Right Way for Evaluating Large Vision-Language Models?; Lin Chen et al
• MMInA: Benchmarking Multihop Multimodal Internet Agents; Ziniu Zhang et al
• A Multimodal Automated Interpretability Agent; Tamar Rott Shaham et al
• MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI; Kaining Ying et al
• ConvBench: A Multi-Turn Conversation Evaluation Benchmark with Hierarchical Capability for Large Vision-Language Models; Shuo Liu et al
• Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models; Piotr Padlewski et al
• What matters when building vision-language models?; Hugo Laurençon et al
• Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions; Junzhang Liu et al

Multimodal Foundation Model

• MotionGPT: Human Motion as a Foreign Language; Biao Jiang et al
• Meta-Transformer: A Unified Framework for Multimodal Learning; Yiyuan Zhang et al
• 3D-LLM: Injecting the 3D World into Large Language Models; Yining Hong et al
• BuboGPT: Enabling Visual Grounding in Multi-Modal LLMs; Yang Zhao et al
• VIT-LENS: Towards Omni-modal Representations; Weixian Lei et al
• LLASM: LARGE LANGUAGE AND SPEECH MODEL; Yu Shu et al
• Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following; Ziyu Guo et al
• NExT-GPT: Any-to-Any Multimodal LLM; Shengqiong Wu et al
• ImageBind-LLM: Multi-modality Instruction Tuning; Jiaming Han et al
• LAURAGPT: LISTEN, ATTEND, UNDERSTAND, AND REGENERATE AUDIO WITH GPT; Jiaming Wang et al
• AN EMBODIED GENERALIST AGENT IN 3D WORLD; Jiangyong Huang et al
• VIT-LENS-2: Gateway to Omni-modal Intelligence; Weixian Lei et al
• CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation; Zineng Tang et al
• X-InstructBLIP: A Framework for aligning X-Modal instruction-aware representations to LLMs and Emergent Cross-modal Reasoning; Artemis Panagopoulou et al
• Merlin: Empowering Multimodal LLMs with Foresight Minds; En Yu et al
• OneLLM: One Framework to Align All Modalities with Language; Jiaming Han et al
• Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action; Jiasen Lu et al
• WORLD MODEL ON MILLION-LENGTH VIDEO AND LANGUAGE WITH RINGATTENTION; Hao Liu et al
• LLMBind: A Unified Modality-Task Integration Framework; Bin Zhu et al
• Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts; Yunxin Li et al

Image Generation

• Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors; Oran Gafni et al
• Modeling Image Composition for Complex Scene Generation; Zuopeng Yang et al
• Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis; Wan-Cyuan Fan et al
• ReCo: Region-Controlled Text-to-Image Generation; Zhengyuan Yang et al
• UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild; Can Qin et al
• Going Beyond Nouns With Vision & Language Models Using Synthetic Data; Paola Cascante-Bonilla et al
• GUIDING INSTRUCTION-BASED IMAGE EDITING VIA MULTIMODAL LARGE LANGUAGE MODELS; Tsu-Jui Fu et al
• KOSMOS-G: Generating Images in Context with Multimodal Large Language Models; Xichen Pan et al
• DiagrammerGPT: Generating Open-Domain, Open-Platform Diagrams via LLM Planning; Abhay Zala et al
• LLMGA: Multimodal Large Language Model based Generation Assistant; Bin Xia et al
• ChatIllusion: Efficient-Aligning Interleaved Generation ability with Visual Instruction Model; Xiaowei Chi et al
• Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition; Chun-Hsiao Yeh et al
• Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers; Peng Gao et al

Document Understanding

• LayoutLM: Pre-training of Text and Layout for Document Image Understanding; Yiheng Xu et al
• LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding; Yang Xu et al
• LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking; Yupan Huang et al
• StrucTexT: Structured Text Understanding with Multi-Modal Transformers; Yulin Li et al
• LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding; Jiapeng Wang et al
• PIX2STRUCT: SCREENSHOT PARSING AS PRETRAINING FOR VISUAL LANGUAGE UNDERSTANDING; Kenton Lee et al
• Unifying Vision, Text, and Layout for Universal Document Processing; Zineng Tang et al
• STRUCTEXTV2: MASKED VISUAL-TEXTUAL PREDIC- TION FOR DOCUMENT IMAGE PRE-TRAINING; Yuechen Yu et al
• UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning; Ahmed Masry et al
• Cream: Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models; Geewook Kim et al
• LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding; Yi Tu et al
• mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding; Jiabo Ye et al
• KOSMOS-2.5: A Multimodal Literate Model; Tengchao Lv et al
• STRUCTCHART: PERCEPTION, STRUCTURING, REASONING FOR VISUAL CHART UNDERSTANDING; Renqiu Xia et al
• UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model; Jiabo Ye et al
• MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning; Fuxiao Liu et al
• ChartLlama: A Multimodal LLM for Chart Understanding and Generation; Yucheng Han et al
• G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model; Jiahui Gao et al
• ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning; Fanqing Meng et al
• ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning; Renqiu Xia et al
• Enhancing Vision-Language Pre-training with Rich Supervisions; Yuan Gao et al
• TextMonkey: An OCR-Free Large Multimodal Model for Understanding Document; Yuliang Liu et al
• ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning; Ahmed Masry et al
• Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMs; Yonghui Wang et al
• Chart-based Reasoning: Transferring Capabilities from LLMs to VLMs; Victor Carbune et al
• HRVDA: High-Resolution Visual Document Assistant; Chaohu Liu et al
• TextSquare: Scaling up Text-Centric Visual Instruction Tuning; Jingqun Tang et al
• TinyChart: Efficient Chart Understanding with Visual Token Merging and Program-of-Thoughts Learning; Liang Zhang et al
• Exploring the Capabilities of Large Multimodal Models on Dense Text; Shuo Zhang et al

Dataset

• A Diagram Is Worth A Dozen Images; Aniruddha Kembhavi et al
• ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning; Ahmed Masry et al
• PDF-VQA: A New Dataset for Real-World VQA on PDF Documents; Yihao Ding et al
• DocumentNet: Bridging the Data Gap in Document Pre-Training; Lijun Yu et al
• Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning; Kung-Hsiang Huang et al

Table

• Visual Understanding of Complex Table Structures from Document Images; Sachin Raja et al
• Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modeling; Yongshuai Huang et al
• Table-GPT: Table-tuned GPT for Diverse Table Tasks; Peng Li et al
• TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios; Xiaokang Zhang et al

Tool Learning

NLP

• TALM: Tool Augmented Language Models; Aaron Paris et al
• WebGPT: Browser-assisted question-answering with human feedback; Reiichiro Nakano et al
• LaMDA: Language Models for Dialog Applications; Romal Thoppilan et al
• BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage; Kurt Shuster et al
• PAL: program-aided language models; Luyu Gao et al
• Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks; Wenhu Chen et al
• A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level; Iddo Droria et al
• React: synergizing reasoning and acting in language models; Shunyu Yao et al
• MIND’S EYE: GROUNDED LANGUAGE MODEL REASONING THROUGH SIMULATION; Ruibo Liu et al
• Toolformer: Language Models Can Teach Themselves to Use Tools; Timo Schick et al
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback; Baolin Peng et al
• ART: Automatic multi-step reasoning and tool-use for large language models; Bhargavi Paranjape et al
• Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models; Pan Lu et al
• AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head; Rongjie Huang et al
• Augmented Large Language Models with Parametric Knowledge Guiding; Ziyang Luo et al
• COOK: Empowering General-Purpose Language Models with Modular and Collaborative Knowledge; Shangbin Feng et al
• StructGPT: A General Framework for Large Language Model to Reason over Structured Data; Jinhao Jiang et al
• Chain of Knowledge: A Framework for Grounding Large Language Models with Structured Knowledge Bases; Xingxuan Li et al
• CREATOR: Disentangling Abstract and Concrete Reasonings of Large Language Models through Tool Creation; Cheng Qian et al
• ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases; Qiaoyu Tang et al
• WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences; Xiao Liu et al
• RestGPT: Connecting Large Language Models with Real-World Applications via RESTful APIs; Yifan Song et al
• MIND2WEB: Towards a Generalist Agent for the Web; Xiang Deng et al
• Certified Reasoning with Language Models; Gabriel Poesia et al
• ToolQA: A Dataset for LLM Question Answering with External Tools; Yuchen Zhuang et al
• On the Tool Manipulation Capability of Open-source Large Language Models; Qiantong Xu, Fenglu Hong, Bo Li, Changran Hu, Zhengyu Chen, Jian Zhang et al
• CHATDB: AUGMENTING LLMS WITH DATABASES AS THEIR SYMBOLIC MEMORY; Chenxu Hu et al
• MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting; Tatsuro Inaba et al
• Making Language Models Better Tool Learners with Execution Feedback; Shuofei Qiao et al
• CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing; Zhibin Gou et al
• ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models; Zhipeng Chen et al
• Fact-Checking Complex Claims with Program-Guided Reasoning; Liangming Pan et al
• Gorilla: Large Language Model Connected with Massive APIs; Shishir G. Patil et al
• ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings; Shibo Hao et al
• Large Language Models as Tool Makers; Tianle Cai et al
• VOYAGER: An Open-Ended Embodied Agent with Large Language Models; Guanzhi Wang et al
• FACTOOL: Factuality Detection in Generative AI A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios; I-Chun Chern et al
• WebArena: A Realistic Web Environment for Building Autonomous Agents; Shuyan Zhou et al
• TOOLLLM: FACILITATING LARGE LANGUAGE MODELS TO MASTER 16000+ REAL-WORLD APIS; Yujia Qin et al
• Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models; Cheng-Yu Hsieh et al
• ExpeL: LLM Agents Are Experiential Learners; Andrew Zhao et al
• Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum; Shen Gao et al
• Self-driven Grounding: Large Language Model Agents with Automatical Language-aligned Skill Learning; Shaohui Peng et al
• Identifying the Risks of LM Agents with an LM-Emulated Sandbox; Yangjun Ruan et al
• TORA: A TOOL-INTEGRATED REASONING AGENT FOR MATHEMATICAL PROBLEM SOLVING; Zhibin Gou et al
• CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets; Lifan Yuan et al
• METATOOL BENCHMARK: DECIDING WHETHER TO USE TOOLS AND WHICH TO USE; Yue Huang et al
• A Comprehensive Evaluation of Tool-Assisted Generation Strategies; Alon Jacovi et al
• TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems; Yilun Kong et al
• GITAGENT: FACILITATING AUTONOMOUS AGENT WITH GITHUB BY TOOL EXTENSION; Bohan Lyu et al
• TROVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Tasks; Zhiruo Wang et al
• Towards Uncertainty-Aware Language Agent; Jiuzhou Han et al
• Tool-LMM: A Large Multi-Modal Model for Tool Agent Learning; Chenyu Wang et al
• Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills; Kolby Nottingham et al
• AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls; Yu Du et al
• SCIAGENT: Tool-augmented Language Models for Scientific Reasoning; Yubo Ma et al
• API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs; Kinjal Basu et al
• Empowering Large Language Model Agents through Action Learning; Haiteng Zhao et al
• LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error; Boshi Wang et al
• StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models; Zhicheng Guo et al

With Visual Tools

• Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models; Chenfei Wu et al
• ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions; Deyao Zhu et al
• Video ChatCaptioner: Towards Enriched Spatiotemporal Descriptions; Jun Chen et al
• Visual Programming: Compositional visual reasoning without training; Tanmay Gupta et al
• ViperGPT: Visual Inference via Python Execution for Reasoning; Dídac Surís et al
• Chat with the Environment: Interactive Multimodal Perception using Large Language Models; Xufeng Zhao et al
• MM-REACT : Prompting ChatGPT for Multimodal Reasoning and Action; Zhengyuan Yang et al
• HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace; Yongliang Shen et al
• TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs; Yaobo Liang et al
• OpenAGI: When LLM Meets Domain Experts; Yingqiang Ge et al; Benchmark.
• Inner Monologue: Embodied Reasoning through Planning with Language Models; Wenlong Huang et al
• Caption Anything: Interactive Image Description with Diverse Multimodal Controls; Teng Wang et al
• InternChat: Solving Vision-Centric Tasks by Interacting with Chatbots Beyond Language; Zhaoyang Liu et al
• Modular Visual Question Answering via Code Generation; Sanjay Subramanian et al
• Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language; William Berrios et al
• AVIS: Autonomous Visual Information Seeking with Large Language Models; Ziniu Hu et al
• AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn; Difei Gao et al
• GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction; Rui Yang et al
• LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent; Jianing Yang et al
• Idea2Img: Iterative Self-Refinement with GPT-4V(ision) for Automatic Image Design and Generation; Zhengyuan Yang et al
• ControlLLM: Augment Language Models with Tools by Searching on Graphs; Zhaoyang Liu et al
• MM-VID: Advancing Video Understanding with GPT-4V(ision); Kevin Lin et al
• Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models; Yushi Hu et al
• CogCoM: Train Large Vision-Language Models Diving into Details through Chain of Manipulations; Ji Qi et al
• CLOVA: a closed-loop visual assistant with tool usage and update; Zhi Gao et al
• m&m’s: A Benchmark to Evaluate Tool-Use for multi-step multi-modal Tasks; Zixian Ma et al
• Plug-and-Play Grounding of Reasoning in Multimodal Large Language Models; Jiaxing Chen et al

Instruction Tuning

• Cross-Task Generalization via Natural Language Crowdsourcing Instructions; Swaroop Mishra et al
• FINETUNED LANGUAGE MODELS ARE ZERO-SHOT LEARNERS; Jason Wei et al
• MULTITASK PROMPTED TRAINING ENABLES ZERO-SHOT TASK GENERALIZATION; Victor Sanh et al
• Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks; Yizhong Wang et al
• Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization; Yuxian Gu et al
• Scaling Instruction-Finetuned Language Models; Hyung Won Chung et al
• Task-aware Retrieval with Instructions; Akari Asai et al
• One Embedder, Any Task: Instruction-Finetuned Text Embeddings; Hongjin Su et al
• Boosting Natural Language Generation from Instructions with Meta-Learning; Budhaditya Deb et al
• Exploring the Benefits of Training Expert Language Models over Instruction Tuning; Joel Jang et al
• OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization; Srinivasan Iyer et al
• Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor; Or Honovich et al
• WeaQA: Weak Supervision via Captions for Visual Question Answering; Pratyay Banerjee et al
• MULTIINSTRUCT: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning; Zhiyang Xu et al
• SELF-INSTRUCT: Aligning Language Model with Self Generated Instructions; Yizhong Wang et al
• Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases; Yunjie Ji et al
• INSTRUCTION TUNING WITH GPT-4; Baolin Peng et al
• The Flan Collection: Designing Data and Methods for Effective Instruction Tuning; Shayne Longpre et al
• LongForm: Optimizing Instruction Tuning for Long Text Generation with Corpus Extraction; Abdullatif Köksal et al
• GUESS THE INSTRUCTION! FLIPPED LEARNING MAKES LANGUAGE MODELS STRONGER ZERO-SHOT LEARNERS; Seonghyeon Ye et al
• In-Context Instruction Learning; Seonghyeon Ye et al
• WizardLM: Empowering Large Language Models to Follow Complex Instructions; Can Xu et al
• Controlled Text Generation with Natural Language Instructions; Wangchunshu Zhou et al
• Poisoning Language Models During Instruction Tuning; Alexander Wan et al
• Improving Cross-Task Generalization with Step-by-Step Instructions; Yang Wu et al
• VideoChat: Chat-Centric Video Understanding; KunChang Li et al
• SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities; Dong Zhang et al
• Prompting with Pseudo-Code Instructions; Mayank Mishra et al
• LIMA: Less Is More for Alignment; Chunting Zhou et al
• ExpertPrompting: Instructing Large Language Models to be Distinguished Experts; Benfeng Xu et al
• HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot Generalisation; Hamish Ivison et al
• Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models; Gen Luo et al
• SAIL: Search-Augmented Instruction Learning; Hongyin Luo et al
• Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning; Fan Yin et al
• DYNOSAUR: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation; Da Yin et al
• MACAW-LLM: MULTI-MODAL LANGUAGE MODELING WITH IMAGE, AUDIO, VIDEO, AND TEXT INTEGRATION; Chenyang Lyu et al
• How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources; Yizhong Wang et al
• INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models; Yew Ken Chia et al
• MIMIC-IT: Multi-Modal In-Context Instruction Tuning; Bo Li et al
• Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning; Fuxiao Liu et al
• M3IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning; Lei Li et al
• InstructEval: Systematic Evaluation of Instruction Selection Methods; Anirudh Ajith et al
• LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark; Zhenfei Yin et al
• Instruction Mining: High-Quality Instruction Data Selection for Large Language Models; Yihan Cao et al
• ALPAGASUS: TRAINING A BETTER ALPACA WITH FEWER DATA; Lichang Chen et al
• Exploring Format Consistency for Instruction Tuning; Shihao Liang et al
• Self-Alignment with Instruction Backtranslation; Xian Li et al
• #INSTAG: INSTRUCTION TAGGING FOR DIVERSITY AND COMPLEXITY ANALYSIS; Keming Lu et al
• CITING: LARGE LANGUAGE MODELS CREATE CURRICULUM FOR INSTRUCTION TUNING; Tao Feng et al
• Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models; Haoran Li et al

Incontext Learning

• Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?; Sewon Min et al
• Extrapolating to Unnatural Language Processing with GPT-3's In-context Learning: The Good, the Bad, and the Mysterious; Frieda Rong et al
• Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning; Haokun Liu et al
• Learning To Retrieve Prompts for In-Context Learning; Ohad Rubin et al
• An Explanation of In-context Learning as Implicit Bayesian Inference; Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma
• MetaICL: Learning to Learn In Context; Sewon Min et al
• PROMPTING GPT-3 TO BE RELIABLE; Chenglei Si et al
• Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm; Laria Reynolds et al
• Do Prompt-Based Models Really Understand the Meaning of their Prompts?; Albert Webson et al
• On the Relation between Sensitivity and Accuracy in In-context Learning; Yanda Chen et al
• Meta-learning via Language Model In-context Tuning; Yanda Chen et al
• Extrapolating to Unnatural Language Processing with GPT-3's In-context Learning: The Good, the Bad, and the Mysterious; Frieda Rong
• SELECTIVE ANNOTATION MAKES LANGUAGE MODELS BETTER FEW-SHOT LEARNERS; Hongjin Su et al
• Robustness of Demonstration-based Learning Under Limited Data Scenario; Hongxin Zhang et al; Demonstration-based learning, tuning the parameters.
• Active Example Selection for In-Context Learning; Yiming Zhang et al
• Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity; Yao Lu et al
• Calibrate Before Use: Improving Few-Shot Performance of Language Models; Tony Z. Zhao et al
• DIALOGIC: Controllable Dialogue Simulation with In-Context Learning; Zekun Li et al
• PRESERVING IN-CONTEXT LEARNING ABILITY IN LARGE LANGUAGE MODEL FINE-TUNING; Yihan Wang et al
• Teaching Algorithmic Reasoning via In-context Learning; Hattie Zhou et al
• On the Compositional Generalization Gap of In-Context Learning Arian Hosseini et al
• Transformers generalize differently from information stored in context vs weights; Stephanie C.Y. Chan et al
• OVERTHINKING THE TRUTH: UNDERSTANDING HOW LANGUAGE MODELS PROCESS FALSE DEMONSTRATIONS; Anonymous
• In-context Learning and Induction Heads; Catherine Olsson et al
• Complementary Explanations for Effective In-Context Learning; Xi Ye et al
• What is Not in the Context? Evaluation of Few-shot Learners with Informative Demonstrations; Michal Štefánik et al
• Robustness of Learning from Task Instructions; Jiasheng Gu et al
• Structured Prompting: Scaling In-Context Learning to 1,000 Examples; Yaru Hao et al
• Transformers learn in-context by gradient descent; Johannes von Oswald et al
• Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale; Hritik Bansal et al
• Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations; Xinxi Lyu et al
• Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters; Boshi Wang et al
• Careful Data Curation Stabilizes In-context Learning; Ting-Yun Chang et al
• Parallel Context Windows Improve In-Context Learning of Large Language Models; Nir Ratner et al
• Investigating Fusion Methods for In-Context Learning; Qinyuan Ye et al
• Batch Prompting: Efficient Inference with Large Language Model APIs; Zhoujun Cheng et al
• Explanation Selection Using Unlabeled Data for In-Context Learning; Xi Ye et al
• Compositional Exemplars for In-context Learning; Jiacheng Ye et al
• Distinguishability Calibration to In-Context Learning; Hongjing Li et al
• How Does In-Context Learning Help Prompt Tuning?; Simeng Sun et al
• Guiding Large Language Models via Directional Stimulus Prompting; Zekun Li et al
• In-Context Instruction Learning; Seonghyeon Ye et al
• LARGER LANGUAGE MODELS DO IN-CONTEXT LEARNING DIFFERENTLY; Jerry Wei et al
• kNN PROMPTING: BEYOND-CONTEXT LEARNING WITH CALIBRATION-FREE NEAREST NEIGHBOR INFERENCE; Benfeng Xu et al
• Learning In-context Learning for Named Entity Recognition; Jiawei Chen et al
• SELF-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations; Wei-Lin Chen et al
• Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation; Marius Mosbach et al
• Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning; Ruixiang Tang et al
• IN-CONTEXT REINFORCEMENT LEARNING WITH ALGORITHM DISTILLATION; Michael Laskin et al
• Supervised Pretraining Can Learn In-Context Reinforcement Learning; Jonathan N. Lee et al
• Learning to Retrieve In-Context Examples for Large Language Models; Liang Wang et al
• IN-CONTEXT LEARNING IN LARGE LANGUAGE MODELS LEARNS LABEL RELATIONSHIPS BUT IS NOT CONVENTIONAL LEARNING; Jannik Kossen et al
• In-Context Alignment: Chat with Vanilla Language Models Before Fine-Tuning; Xiaochuang Han et al

Learning from Feedback

• Decision Transformer: Reinforcement Learning via Sequence Modeling; Lili Chen et al
• Quark: Controllable Text Generation with Reinforced (Un)learning; Ximing Lu et al
• Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedback; Niket Tandon et al
• MemPrompt: Memory-assisted Prompt Editing with User Feedback; Aman Madaan et al
• Training language models to follow instructions with human feedback; Long Ouyang et al
• Pretraining Language Models with Human Preferences; Tomasz Korbak et al
• Training Language Models with Language Feedback; Jérémy Scheurer et al
• Training Language Models with Language Feedback at Scale; Jérémy Scheurer et al
• Improving Code Generation by Training with Natural Language Feedback; Angelica Chen et al
• REFINER: Reasoning Feedback on Intermediate Representations; Debjit Paul et al
• RRHF: Rank Responses to Align Language Models with Human Feedback without tears; Zheng Yuan et al
• Constitutional AI: Harmlessness from AI Feedback; Yuntao Bai et al
• Chain of Hindsight Aligns Language Models with Feedback; Hao Liu et al
• Self-Edit: Fault-Aware Code Editor for Code Generation; Kechi Zhang et al
• RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs; Afra Feyza Akyürek et al
• Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing; Hao Yan et al
• Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback; Yao Fu et al
• Fine-Grained Human Feedback Gives Better Rewards for Language Model Training; Zeqiu Wu et al
• Aligning Large Language Models through Synthetic Feedback; Sungdong Kim1 et al
• Improving Language Models via Plug-and-Play Retrieval Feedback; Wenhao Yu et al
• Improving Open Language Models by Learning from Organic Interactions; Jing Xu et al
• Demystifying GPT Self-Repair for Code Generation; Theo X. Olausson et al
• Reflexion: Language Agents with Verbal Reinforcement Learning; Noah Shinn et al
• Evaluating Language Models for Mathematics through Interactions; Katherine M. Collins et al
• InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback; John Yang et al
• System-Level Natural Language Feedback; Weizhe Yuan et al
• Preference Ranking Optimization for Human Alignment; Feifan Song et al
• Let Me Teach You: Pedagogical Foundations of Feedback for Language Models; Beatriz Borges et al
• AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback; Yann Dubois et al
• Training Socially Aligned Language Models in Simulated Human Society; Ruibo Liu et al
• RLTF: Reinforcement Learning from Unit Test Feedback; Jiate Liu et al
• Chain of Hindsight Aligns Language Models with Feedback; Hao Liu et al
• LETI: Learning to Generate from Textual Interactions; Xingyao Wang et al
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model; Rafael Rafailov et al
• FigCaps-HF: A Figure-to-Caption Generative Framework and Benchmark with Human Feedback; Ashish Singh et al
• Leveraging Implicit Feedback from Deployment Data in Dialogue; Richard Yuanzhe Pang et al
• RLCD: REINFORCEMENT LEARNING FROM CONTRAST DISTILLATION FOR LANGUAGE MODEL ALIGNMENT; Kevin Yang et al
• Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback; Viet Dac Lai et al
• Reinforced Self-Training (ReST) for Language Modeling; Caglar Gulcehre et al
• EVERYONE DESERVES A REWARD: LEARNING CUSTOMIZED HUMAN PREFERENCES; Pengyu Cheng et al
• RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback; Harrison Lee et al
• STABILIZING RLHF THROUGH ADVANTAGE MODEL AND SELECTIVE REHEARSAL; Baolin Peng et al
• OPENCHAT: ADVANCING OPEN-SOURCE LANGUAGE MODELS WITH MIXED-QUALITY DATA; Guan Wang et al
• HUMAN FEEDBACK IS NOT GOLD STANDARD; Tom Hosking et al
• A LONG WAY TO GO: INVESTIGATING LENGTH CORRELATIONS IN RLHF; Prasann Singhal et al
• CHAT VECTOR: A SIMPLE APPROACH TO EQUIP LLMS WITH NEW LANGUAGE CHAT CAPABILITIES; Shih-Cheng Huang et al
• SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF; Yi Dong et al
• UNDERSTANDING THE EFFECTS OF RLHF ON LLM GENERALISATION AND DIVERSITY; Robert Kirk et al
• GAINING WISDOM FROM SETBACKS: ALIGNING LARGE LANGUAGE MODELS VIA MISTAKE ANALYSIS; Kai Chen et al
• Tuna: Instruction Tuning using Feedback from Large Language Models; Haoran Li et al
• Teaching Language Models to Self-Improve through Interactive Demonstrations; Xiao Yu et al
• Democratizing Reasoning Ability: Tailored Learning from Large Language Model; Zhaoyang Wang et al
• ENABLE LANGUAGE MODELS TO IMPLICITLY LEARN SELF-IMPROVEMENT FROM DATA; Ziqi Wang et al
• ULTRAFEEDBACK: BOOSTING LANGUAGE MODELS WITH HIGH-QUALITY FEEDBACK; Ganqu Cui et al
• HELPSTEER: Multi-attribute Helpfulness Dataset for STEERLM; Zhilin Wang et al
• Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering; Yichi Zhang et al
• TOOL-AUGMENTED REWARD MODELING; Lei Li et al
• Nash Learning from Human Feedback; Rémi Munos et al
• Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models; Avi Singh et al
• When Life Gives You Lemons, Make Cherryade: Converting Feedback from Bad Responses into Good Labels; Weiyan Shi et al
• ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles; Savvas Petridis et al
• REASONS TO REJECT? ALIGNING LANGUAGE MODELS WITH JUDGMENTS; Weiwen Xu et al
• Some things are more CRINGE than others: Preference Optimization with the Pairwise Cringe Loss; Jing Xu et al
• Mitigating Unhelpfulness in Emotional Support Conversations with Multifaceted AI Feedback; Jiashuo Wang et al
• Secrets of RLHF in Large Language Models Part II: Reward Modeling; Binghai Wang et al
• Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation; Haoran Xu et al
• WARM: On the Benefits of Weight Averaged Reward Models; Alexandre Ramé et al
• Self-Rewarding Language Models; Weizhe Yuan et al
• Dense Reward for Free in Reinforcement Learning from Human Feedback; Alex J. Chan et al
• Efficient Exploration for LLMs; Vikranth Dwaracherla et al
• KTO: Model Alignment as Prospect Theoretic Optimization; Kawin Ethayarajh et al
• LiPO: Listwise Preference Optimization through Learning-to-Rank; Tianqi Liu et al
• Direct Language Model Alignment from Online AI Feedback; Shangmin Guo et al
• Noise Contrastive Alignment of Language Models with Explicit Rewards; Huayu Chen et al
• RLVF: Learning from Verbal Feedback without Overgeneralization; Moritz Stephan et al
• OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement; Tianyu Zheng et al
• A Critical Evaluation of AI Feedback for Aligning Large Language Models; Archit Sharma et al
• VOLCANO: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision; Seongyun Lee et al
• Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward; Ruohong Zhang et al
• ChatGLM-RLHF: Practices of Aligning Large Language Models with Human Feedback; Zhenyu Hou et al
• From r to Q: Your Language Model is Secretly a Q-Function; Rafael Rafailov* et al
• Aligning LLM Agents by Learning Latent Preference from User Edits; Ge Gao et al
• Self-Play Preference Optimization for Language Model Alignment; Yue Wu et al
• Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data; Fahim Tajwar et al

Video Foundation Model

• VideoBERT: A Joint Model for Video and Language Representation Learning; Chen Sun et al
• LEARNING VIDEO REPRESENTATIONS USING CONTRASTIVE BIDIRECTIONAL TRANSFORMER; Chen Sun et al
• End-to-End Learning of Visual Representations from Uncurated Instructional Videos; Antoine Miech et al
• HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training; Linjie Li et al
• Multi-modal Transformer for Video Retrieval; Valentin Gabeur et al
• ActBERT: Learning Global-Local Video-Text Representations; Linchao Zhu et al
• Spatiotemporal Contrastive Video Representation Learning; Rui Qian et al
• DECEMBERT: Learning from Noisy Instructional Videos via Dense Captions and Entropy Minimization; Zineng Tang et al
• HiT: Hierarchical Transformer with Momentum Contrast for Video-Text Retrieval; Song Liu et al
• Self-Supervised MultiModal Versatile Networks; Jean-Baptiste Alayrac et al
• COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning; Simon Ging et al
• VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning; Hao Tan et al
• Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling; Jie Lei et al
• Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval; Max Bain et al
• CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval; Huaishao Luo et al
• MERLOT: Multimodal Neural Script Knowledge Models; Rowan Zellers et al
• VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text; Hassan Akbari et al
• VIOLET: End-to-End Video-Language Transformers with Masked Visual-token Modeling; Tsu-Jui Fu et al
• CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising; Jianjie Luo et al
• LAVENDER: Unifying Video-Language Understanding as Masked Language Modeling; Linjie Li et al
• CLIP-VIP: ADAPTING PRE-TRAINED IMAGE-TEXT MODEL TO VIDEO-LANGUAGE ALIGNMENT; Hongwei Xue et al
• Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning; Rui Wang et al
• Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning; Yuchong Sun et al
• Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning; Antoine Yang et al
• InternVideo: General Video Foundation Models via Generative and Discriminative Learning; Yi Wang et al
• MINOTAUR: Multi-task Video Grounding From Multimodal Queries; Raghav Goyal et al
• VideoLLM: Modeling Video Sequence with Large Language Models; Guo Chen et al
• COSA: Concatenated Sample Pretrained Vision-Language Foundation Model; Sihan Chen et al
• VALLEY: VIDEO ASSISTANT WITH LARGE LANGUAGE MODEL ENHANCED ABILITY; Ruipu Luo et al
• Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models; Muhammad Maaz et al
• Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding; Hang Zhang et al
• InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation; Yi Wang et al
• VideoCon: Robust Video-Language Alignment via Contrast Captions; Hritik Bansal et al
• PG-Video-LLaVA: Pixel Grounding Large Video-Language Models; Shehan Munasinghe et al
• VLM-Eval: A General Evaluation on Video Large Language Models; Shuailin Li et al
• Video-LLaVA: Learning United Visual Representation by Alignment Before Projection; Bin Lin et al
• MVBench: A Comprehensive Multi-modal Video Understanding Benchmark; Kunchang Li et al
• LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models; Yanwei Li et al
• LANGUAGEBIND: EXTENDING VIDEO-LANGUAGE PRETRAINING TO N-MODALITY BY LANGUAGE-BASED SEMANTIC ALIGNMENT; Bin Zhu et al
• TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding; Shuhuai Ren et al
• Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers; Tsai-Shien Chen et al
• INTERNVIDEO2: SCALING VIDEO FOUNDATION MODELS FOR MULTIMODAL VIDEO UNDERSTANDING; Yi Wang et al
• PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning; Lin Xu et al
• LLAVA FINDS FREE LUNCH: TEACHING HUMAN BEHAVIOR IMPROVES CONTENT UNDERSTANDING ABILITIES OF LLMS; Somesh Singh et al
• Pandora: Towards General World Model with Natural Language Actions and Video States; Jiannan Xiang et al

Key Frame Detection

• Self-Supervised Learning to Detect Key Frames in Videos; Xiang Yan et al
• Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training; Dezhao Luo et al
• Localizing Moments in Long Video Via Multimodal Guidance; Wayner Barrios et al

Vision Model

• PIX2SEQ: A LANGUAGE MODELING FRAMEWORK FOR OBJECT DETECTION; Ting Chen et al
• Scaling Vision Transformers to 22 Billion Parameters; Mostafa Dehghani et al
• CLIPPO: Image-and-Language Understanding from Pixels Only; Michael Tschannen et al
• Segment Anything; Alexander Kirillov et al
• InstructDiffusion: A Generalist Modeling Interface for Vision Tasks; Zigang Geng et al
• RMT: Retentive Networks Meet Vision Transformers; Qihang Fan et al
• INSTRUCTCV: INSTRUCTION-TUNED TEXT-TO-IMAGE DIFFUSION MODELS AS VISION GENERALISTS; Yulu Gan et al
• Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks; Micah Goldblum et al
• RECOGNIZE ANY REGIONS; Haosen Yang et al
• AiluRus: A Scalable ViT Framework for Dense Prediction; Jin Li et al
• T-Rex: Counting by Visual Prompting; Qing Jiang et al
• Visual In-Context Prompting; Feng Li et al
• SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding; Haoxiang Wang et al
• Sequential Modeling Enables Scalable Learning for Large Vision Models; Yutong Bai et al
• Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks; Bin Xiao et al
• 4M: Massively Multimodal Masked Modeling; David Mizrahi et al
• InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks; Zhe Chen et al
• Scalable Pre-training of Large Autoregressive Image Models; Alaaeldin El-Nouby et al
• When Do We Not Need Larger Vision Models?; Baifeng Shi et al
• ViTamin: Designing Scalable Vision Models in the Vision-Language Era; Jieneng Chen et al

Pretraining

• MDETR - Modulated Detection for End-to-End Multi-Modal Understanding; Aishwarya Kamath et al
• SGEITL: Scene Graph Enhanced Image-Text Learning for Visual Commonsense Reasoning; Zhecan Wang et al; Incorporating scene graphs in pretraining and fine-tuning improves performance of VCR tasks.
• ERNIE-ViL: Knowledge Enhanced Vision-Language Representations through Scene Graphs; Fei Yu et al
• KB-VLP: Knowledge Based Vision and Language Pretraining; Kezhen Chen et al; Propose to distill the object knowledge in VL pretraining for object-detector-free VL foundation models; Pretraining tasks include predicting the RoI features, category, and learning the alignments between phrases and image regions.
• Large-Scale Adversarial Training for Vision-and-Language Representation Learning; Zhe Gan et al
• Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts; Yan Zeng et al
• BEIT: BERT Pre-Training of Image Transformers; Hangbo Bao et al; Pre-trained CV model.
• BEIT V2: Masked Image Modeling with Vector-Quantized Visual Tokenizers; Zhiliang Peng et al; Pre-trained CV model.
• VirTex: Learning Visual Representations from Textual Annotations; Karan Desai et al; Pretraining CV models through the dense image captioning task.
• Florence: A New Foundation Model for Computer Vision; Lu Yuan et al; Pre-trained CV model.
• Grounded Language-Image Pre-training; Liunian Harold Li et al; Learning object-level, language-aware, and semantic-rich visual representations. Introducing phrase grounding to the pretraining task and focusing on object detection as the downstream task; Propose GLIP.
• VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix; Teng Wang et al; Using unpaired data for pretraining.
• Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone; Zi-Yi Dou et al
• WRITE AND PAINT: GENERATIVE VISION-LANGUAGE MODELS ARE UNIFIED MODAL LEARNERS; Shizhe Diao et al
• VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining; Junjie Ke et al
• CONTRASTIVE ALIGNMENT OF VISION TO LANGUAGE THROUGH PARAMETER-EFFICIENT TRANSFER LEARNING; Zaid Khan et al
• The effectiveness of MAE pre-pretraining for billion-scale pretraining; Mannat Singh et al
• Retrieval-based Knowledge Augmented Vision Language Pre-training; Jiahua Rao et al

Visual-augmented LM

• Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision; Hao Tan et al
• Imagination-Augmented Natural Language Understanding; Yujie Lu et al
• Visually-augmented language modeling; Weizhi Wang et al
• Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language Models; Taichi Iki et al
• Is BERT Blind? Exploring the Effect of Vision-and-Language Pretraining on Visual Language Understanding; Morris Alper et al
• TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models; Md Kamrul Hasan et al
• Learning to Imagine: Visually-Augmented Natural Language Generation; Tianyi Tang et al

Novel techniques.

• CM3: A CAUSAL MASKED MULTIMODAL MODEL OF THE INTERNET; Armen Aghajanyan et al; Propose to pretrain on large corpus of structured multi-modal documents (CC-NEWS & En-Wikipedia) that can contain both text and image tokens.
• PaLI: A Jointly-Scaled Multilingual Language-Image Model; Xi Chen et al; Investigate the scaling effect of multi-modal models; Pretrained on WebLI that contains text in over 100 languages.
• Retrieval-Augmented Multimodal Language Modeling; Michihiro Yasunaga et al; Consider text generation and image generation tasks.
• Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning; Zhuolin Yang et al
• Teaching Structured Vision & Language Concepts to Vision & Language Models; Sivan Doveh et al
• MATCHA : Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering; Fangyu Liu et al
• Filtering, Distillation, and Hard Negatives for Vision-Language Pre-Training; Filip Radenovic et al; Propose methods to improve zero-shot performance on retrieval and classification tasks through large-scale pre-training.
• Prismer: A Vision-Language Model with An Ensemble of Experts; Shikun Liu et al
• REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory; Ziniu Hu et al

Adaptation of Foundation Model

• Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture; Tanmay Gupta et al
• Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners; Zhenhailong Wang et al
• Multimodal Few-Shot Learning with Frozen Language Models; Maria Tsimpoukelli et al; Use prefix-like image-embedding to stear the text generation process to achieve few-shot learning.
• Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language; Andy Zeng et al
• UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes; Alexander Kolesnikov et al
• META LEARNING TO BRIDGE VISION AND LANGUAGE MODELS FOR MULTIMODAL FEW-SHOT LEARNING; Ivona Najdenkoska et al
• RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-training; Zheng Yuan et al
• Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners; Renrui Zhang et al
• F-VLM: OPEN-VOCABULARY OBJECT DETECTION UPON FROZEN VISION AND LANGUAGE MODELS; Weicheng Kuo et al
• eP-ALM: Efficient Perceptual Augmentation of Language Models; Mustafa Shukor et al
• Transfer Visual Prompt Generator across LLMs; Ao Zhang et al
• Multimodal Web Navigation with Instruction-Finetuned Foundation Models; Hiroki Furuta et al

Prompting

• Learning to Prompt for Vision-Language Models; Kaiyang Zhou et al; Soft prompt tuning. Useing few-shot learning to improve performance on both in-distribution and out-of-distribution data. Few-shot setting.
• Unsupervised Prompt Learning for Vision-Language Models; Tony Huang et al; Soft prompt tuning. Unsupervised setting.
• Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling; Renrui Zhang et al; Few-shot setting.
• CLIP-Adapter: Better Vision-Language Models with Feature Adapters; Peng Gao et al; Few-shot setting.
• Neural Prompt Search; Yuanhan Zhang et al; Explore the combination of LoRA, Adapter, Soft prompt tuning. In full-data, few-shot, and domain shift settings.
• Visual Prompt Tuning; Menglin Jia et al; Soft prompt tuning + head tuning. Show better performance in few-shot and full-data settings than full-parameters tuning. Quite different from the NLP field.
• Prompt Distribution Learning; Yuning Lu et al; Soft prompt tuning. Few-shot setting.
• Conditional Prompt Learning for Vision-Language Models; identify a critical problem of CoOp: the learned context is not generalizable to wider unseen classes within the same dataset; Propose to learn a DNN that can generate for each image an input-conditional token (vector).
• Learning to Prompt for Continual Learning; Zifeng Wang et al; Continual learning setting. Maintain a prompt pool.
• Exploring Visual Prompts for Adapting Large-Scale Models; Hyojin Bahng et al; Employ adversarial reprogramming as visual prompts. Full-data setting.
• Learning multiple visual domains with residual adapters; Sylvestre-Alvise Rebuff et al; Use adapter to transfer pretrained knowledge to multiple domains while freeze the base model parameters. Work in the CV filed & full-data transfer learning.
• Efficient parametrization of multi-domain deep neural networks; Sylvestre-Alvise Rebuff et al; Still use adapter for transfer learning, with more comprehensive empirical study for an ideal choice.
• Prompting Visual-Language Models for Efficient Video Understanding; Chen Ju et al; Video tasks. Few-shots & zero-shots. Soft prompt tuning.
• Visual Prompting via Image Inpainting; Amir Bar et al; In-context learning in CV. Use pretrained masked auto-encoder.
• CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment; Haoyu Song et al; Propose a parameter-efficient tuning method (bias tuning), function well in few-shot setting.
• LEARNING TO COMPOSE SOFT PROMPTS FOR COMPOSITIONAL ZERO-SHOT LEARNING; Nihal V. Nayak et al; zero-shot setting, inject some knowledge in the learning process.
• Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models; Manli Shu et al; Learn soft-prompt in the test-time.
• Multitask Vision-Language Prompt Tuning; Sheng Shen et al; Few-shot.
• A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models; Woojeong Jin et al
• CPT: COLORFUL PROMPT TUNING FOR PRE-TRAINED VISION-LANGUAGE MODELS; Yuan Yao et al; Good few-shot & zero-shot performance on RefCOCO datasets.
• What Makes Good Examples for Visual In-Context Learning?; Yuanhan Zhang et al
• Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery; Yuxin Wen et al
• PLOT: PROMPT LEARNING WITH OPTIMAL TRANSPORT FOR VISION-LANGUAGE MODELS; Guangyi Chen et al
• What does CLIP know about a red circle? Visual prompt engineering for VLMs; Aleksandar Shtedritski et al

Efficiency

• M3SAT: A SPARSELY ACTIVATED TRANSFORMER FOR EFFICIENT MULTI-TASK LEARNING FROM MULTIPLE MODALITIES; Anonymous
• Prompt Tuning for Generative Multimodal Pretrained Models; Hao Yang et al; Implement prefix-tuning in OFA. Try full-data setting and demonstrate comparable performance.
• Fine-tuning Image Transformers using Learnable Memory; Mark Sandler et al; Add soft prompts in each layer. full-data.
• Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks; Jeffrey O. Zhang et al; Transfer learning.
• Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks; Yen-Cheng Liu et al
• Task Residual for Tuning Vision-Language Models; Tao Yu et al
• UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling; Haoyu Lu et al

Analysis

• What Does BERT with Vision Look At? Liunian Harold Li et al
• Visual Referring Expression Recognition: What Do Systems Actually Learn?; Volkan Cirik et al
• Characterizing and Overcoming the Greedy Nature of Learning in Multi-modal Deep Neural Networks; Nan Wu et al; Study the problem of only relying on one certain modality in training when using multi-modal models.
• Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models; Jize Cao et al
• Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning Weixin Liang et al
• How Much Can CLIP Benefit Vision-and-Language Tasks?; Sheng Shen et al; Explore two scenarios: 1) plugging CLIP into task-specific fine-tuning; 2) combining CLIP with V&L pre-training and transferring to downstream tasks. Show the boost in performance when using CLIP as the image encoder.
• Vision-and-Language or Vision-for-Language? On Cross-Modal Influence in Multimodal Transformers; Stella Frank et al
• Controlling for Stereotypes in Multimodal Language Model Evaluation; Manuj Malik et al
• Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on YouTube; Jack Hessel et al
• What is More Likely to Happen Next? Video-and-Language Future Event Prediction; Jie Lei et al

Grounding

• Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models; Bryan A. Plummer et al; A new benchmark dataset, annotating phrase-region correspondences.
• Connecting Vision and Language with Localized Narratives; Jordi Pont-Tuset et al
• MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding; Qinxin Wang et al
• Visual Grounding Strategies for Text-Only Natural Language Processing; Propose to improve the NLP tasks performance by grounding to images. Two methods are proposed.
• Visually Grounded Neural Syntax Acquisition; Haoyue Shi et al
• PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World; Rowan Zellers et al

VQA Task

• WeaQA: Weak Supervision via Captions for Visual Question Answering; Pratyay Banerjee et al
• Don’t Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering; Aishwarya Agrawal et al
• Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA; Qingyi Si et al
• Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning; Qingyi Si et al
• Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training; Anthony Meng Huat Tiong et al
• FROM IMAGES TO TEXTUAL PROMPTS: ZERO-SHOT VQA WITH FROZEN LARGE LANGUAGE MODELS; Jiaxian Guo et al
• SQuINTing at VQA Models: Introspecting VQA Models with Sub-Questions; Ramprasaath R. Selvaraju et al
• Multimodal retrieval-augmented generator for open question answering over images and text; Wenhu Chen et al
• Towards a Unified Model for Generating Answers and Explanations in Visual Question Answering; Chenxi Whitehouse et al
• Modularized Zero-shot VQA with Pre-trained Models; Rui Cao et al
• Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge; Xingyu Fu et al
• Using Visual Cropping to Enhance Fine-Detail Question Answering of BLIP-Family Models; Jiarui Zhang et al
• Zero-shot Visual Question Answering with Language Model Feedback; Yifan Du et al
• Learning to Ask Informative Sub-Questions for Visual Question Answering; Kohei Uehara et al
• Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQA; Elias Stengel-Eskin et al
• Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering; Rabiul Awal et al

VQA Dataset

• VQA: Visual Question Answering; Aishwarya Agrawal et al
• Towards VQA Models That Can Read; Amanpreet Singh et al
• Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering; Yash Goyal et al; VQA-V2.
• MULTIMODALQA: COMPLEX QUESTION ANSWERING OVER TEXT, TABLES AND IMAGES; Alon Talmor et al
• WebQA: Multihop and Multimodal QA; Yingshan Chang et al
• FunQA: Towards Surprising Video Comprehension; Binzhu Xie et al; Used for video foundation model evaluation.
• Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering; Pan Lu et al
• Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?; Yang Chen et al

Cognition

• Inferring the Why in Images; Hamed Pirsiavash et al
• Visual Madlibs: Fill in the blank Image Generation and Question Answering; Licheng Yu et al
• From Recognition to Cognition: Visual Commonsense Reasoning; Rowan Zellers et al; Benchmark dataset, requiring models to go beyond the recognition level to cognition. Need to reason about a still image and give rationales.
• VisualCOMET: Reasoning about the Dynamic Context of a Still Image; Jae Sung Park et al
• The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning; Jack Hessel et al

Knowledge

• Explicit Knowledge-based Reasoning for Visual Question Answering; Peng Wang et al
• FVQA: Fact-based Visual Question Answering; Peng Wang;
• OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge; Kenneth Marino et al

Social Good

• The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes; Douwe Kiela et al; Multi-modal hate-speech detection.
• Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News; Reuben Tan et al; Multi-modal fake news dedetection.
• InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection; Yi R. Fung et al; Cross-modal fake news detection.
• EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection; Yaqing Wang et al
• End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models; Barry Menglong Yao et al
• SAFE: Similarity-Aware Multi-Modal Fake News Detection; Xinyi Zhou et al
• r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection; Kai Nakamura et al; Fake news detection dataset.
• Fact-Checking Meets Fauxtography: Verifying Claims About Images; Dimitrina Zlatkova et al; Claim-Images pairs.
• Prompting for Multimodal Hateful Meme Classification; Rui Cao et al

Application

• MSMO: Multimodal Summarization with Multimodal Output; Junnan Zhu et al
• Re-imagen: Retrieval-augmented text-to-image generator; Wenhu Chen et al
• Large Scale Multi-Lingual Multi-Modal Summarization Dataset; Yash Verma et al
• Retrieval-augmented Image Captioning; Rita Ramos et al
• SYNTHETIC MISINFORMERS: GENERATING AND COMBATING MULTIMODAL MISINFORMATION; Stefanos-Iordanis Papadopoulos et al
• The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training; Gi-Cheon Kang et al
• CapDet: Unifying Dense Captioning and Open-World Detection Pretraining; Yanxin Long et al
• DECAP: DECODING CLIP LATENTS FOR ZERO-SHOT CAPTIONING VIA TEXT-ONLY TRAINING; Wei Li et al
• Align and Attend: Multimodal Summarization with Dual Contrastive Losses; Bo He et al

Benchmark & Evaluation

• Multimodal datasets: misogyny, pornography, and malignant stereotypes; Abeba Birhane et al
• Understanding ME? Multimodal Evaluation for Fine-grained Visual Commonsense; Zhecan Wang et al
• Probing Image–Language Transformers for Verb Understanding; Lisa Anne Hendricks et al
• VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations; Tiancheng Zhao et al
• WHEN AND WHY VISION-LANGUAGE MODELS BEHAVE LIKE BAGS-OF-WORDS, AND WHAT TO DO ABOUT IT?; Mert Yuksekgonul et al
• GRIT: General Robust Image Task Benchmark; Tanmay Gupta et al
• MULTIMODALQA: COMPLEX QUESTION ANSWERING OVER TEXT, TABLES AND IMAGES; Alon Talmor et al
• Test of Time: Instilling Video-Language Models with a Sense of Time; Piyush Bagad et al

Dataset

• Visual Entailment: A Novel Task for Fine-Grained Image Understanding; Ning Xie et al; Visual entailment task. SNLI-VE.
• A Corpus for Reasoning About Natural Language Grounded in Photographs; Alane Suhr et al; NLVR2.
• VLUE: A Multi-Task Benchmark for Evaluating Vision-Language Models; Wangchunshu Zhou et al; VLUE.
• Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning; Piyush Sharma et al
• Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts; Soravit Changpinyo et al
• LAION-5B: An open large-scale dataset for training next generation image-text models; Christoph Schuhmann et al
• Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks; Colin Leong et al
• Find Someone Who: Visual Commonsense Understanding in Human-Centric Grounding; Haoxuan You et al
• MULTIINSTRUCT: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning; Zhiyang Xu et al
• UKnow: A Unified Knowledge Protocol for Common-Sense Reasoning and Vision-Language Pre-training; Biao Gong et al
• HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips; Antoine Miech et al
• Connecting Vision and Language with Video Localized Narratives; Paul Voigtlaender et al
• LAION-5B: An open large-scale dataset for training next generation image-text models; Christoph Schuhmann et al
• MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions; Mattia Soldan et al
• CHAMPAGNE: Learning Real-world Conversation from Large-Scale Web Videos; Seungju Han et al
• WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning; Krishna Srinivasan et al
• Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved With Text; Wanrong Zhu et al
• OpenAssistant Conversations - Democratizing Large Language Model Alignment; Andreas Köpf et al
• TheoremQA: A Theorem-driven Question Answering dataset; Wenhu Chen et al
• MetaCLUE: Towards Comprehensive Visual Metaphors Research; Arjun R. Akula et al
• CAPSFUSION: Rethinking Image-Text Data at Scale; Qiying Yu et al
• RedCaps: Web-curated image-text data created by the people, for the people; Karan Desai et al
• OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents; Hugo Laurençon et al

Robustness

• Domino: Discovering Systematic Errors with Cross-Modal Embeddings; Sabri Eyuboglu et al
• Learning Visually-Grounded Semantics from Contrastive Adversarial Samples; Haoyue Shi et al
• Visually Grounded Reasoning across Languages and Cultures; Fangyu Liu et al
• A Closer Look at the Robustness of Vision-and-Language Pre-trained Models; Linjie Li et al; Compile a list of robustness-VQA datasets.
• ROBUSTNESS ANALYSIS OF VIDEO-LANGUAGE MODELS AGAINST VISUAL AND LANGUAGE PERTURBATIONS; Madeline C. Schiappa et al
• Context-Aware Robust Fine-Tuning; Xiaofeng Mao et al
• Task Bias in Vision-Language Models; Sachit Menon et al
• Are Multimodal Models Robust to Image and Text Perturbations?; Jielin Qiu et al
• CPL: Counterfactual Prompt Learning for Vision and Language Models; Xuehai He et al
• Improving Zero-shot Generalization and Robustness of Multi-modal Models; Yunhao Ge et al
• DIAGNOSING AND RECTIFYING VISION MODELS USING LANGUAGE; Yuhui Zhang et al
• Multimodal Prompting with Missing Modalities for Visual Recognition; Yi-Lun Lee et al

Hallucination&Factuality

• Object Hallucination in Image Captioning; Anna Rohrbach et al
• Learning to Generate Grounded Visual Captions without Localization Supervision; Chih-Yao Ma et al
• On Hallucination and Predictive Uncertainty in Conditional Language Generation; Yijun Xiao et al
• Consensus Graph Representation Learning for Better Grounded Image Captioning; Wenqiao Zhang et al
• Relational Graph Learning for Grounded Video Description Generation; Wenqiao Zhang et al
• Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning; Ali Furkan Biten et al
• Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training; Wenliang Dai et al
• Models See Hallucinations: Evaluating the Factuality in Video Captioning; Hui Liu et al
• Evaluating and Improving Factuality in Multimodal Abstractive Summarization; David Wan et al
• Evaluating Object Hallucination in Large Vision-Language Models; Yifan Li et al
• Do Language Models Know When They’re Hallucinating References?; Ayush Agrawal et al
• Detecting and Preventing Hallucinations in Large Vision Language Models; Anisha Gunjal et al
• DOLA: DECODING BY CONTRASTING LAYERS IMPROVES FACTUALITY IN LARGE LANGUAGE MODELS; Yung-Sung Chuang et al
• A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models; S.M Towhidul Islam Tonmoy et al
• Inference-Time Intervention: Eliciting Truthful Answers from a Language Model; Kenneth Li et al
• FELM: Benchmarking Factuality Evaluation of Large Language Models; Shiqi Chen et al
• Unveiling the Siren’s Song: Towards Reliable Fact-Conflicting Hallucination Detection; Xiang Chen et al
• ANALYZING AND MITIGATING OBJECT HALLUCINATION IN LARGE VISION-LANGUAGE MODELS; Yiyang Zhou et al
• Woodpecker: Hallucination Correction for Multimodal Large Language Models; Shukang Yin et al
• AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation; Junyang Wang et al
• Fine-tuning Language Models for Factuality; Katherine Tian et al
• Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization; Zhiyuan Zhao et al
• RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback; Tianyu Yu et al
• RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models; Yuanhao Wu et al
• Learning to Trust Your Feelings: Leveraging Self-awareness in LLMs for Hallucination Mitigation; Yuxin Liang et al
• Don’t Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration; Shangbin Feng et al
• FLAME: Factuality-Aware Alignment for Large Language Models; Sheng-Chieh Lin et al

Cognitive NeuronScience & Machine Learning

• Mind Reader: Reconstructing complex images from brain activities; Sikun Lin et al
• Joint processing of linguistic properties in brains and language models; Subba Reddy Oota et al
• Is the Brain Mechanism for Hierarchical Structure Building Universal Across Languages? An fMRI Study of Chinese and English; Xiaohan Zhang et al
• TRAINING LANGUAGE MODELS FOR DEEPER UNDERSTANDING IMPROVES BRAIN ALIGNMENT; Khai Loong Aw et al
• Abstract Visual Reasoning with Tangram Shapes; Anya Ji et al
• DISSOCIATING LANGUAGE AND THOUGHT IN LARGE LANGUAGE MODELS: A COGNITIVE PERSPECTIVE; Kyle Mahowald et al
• Language Cognition and Language Computation Human and Machine Language Understanding; Shaonan Wang et al
• From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought; Lionel Wong et al
• DIVERGENCES BETWEEN LANGUAGE MODELS AND HUMAN BRAINS; Yuchen Zhou et al
• Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?; Andreas Opedal et al

Theory of Mind

• Do Large Language Models know what humans know?; Sean Trott et al
• Few-shot Language Coordination by Modeling Theory of Mind; Hao Zhu et al
• Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind; Mo Yu et al
• Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs; Maarten Sap et al
• A Cognitive Evaluation of Instruction Generation Agents tl;dr They Need Better Theory-of-Mind Capabilities; Lingjun Zhao et al
• MINDCRAFT: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks; Cristian-Paul Bara et al
• TVSHOWGUESS: Character Comprehension in Stories as Speaker Guessing; Yisi Sang et al
• Theory of Mind May Have Spontaneously Emerged in Large Language Models; Michal Kosinski
• COMPUTATIONAL LANGUAGE ACQUISITION WITH THEORY OF MIND; Andy Liu et al
• Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of Mind; Ece Takmaz et al
• Understanding Social Reasoning in Language Models with Language Models; Kanishk Gandhi et al
• HOW FAR ARE LARGE LANGUAGE MODELS FROM AGENTS WITH THEORY-OF-MIND?; Pei Zhou et al

Cognitive NeuronScience

• Functional specificity in the human brain: A window into the functional architecture of the mind; Nancy Kanwisher et al
• Visual motion aftereffect in human cortical area MT revealed by functional magnetic resonance imaging; Roger B. H. Tootell et al
• Speed of processing in the human visual system; Simon Thorpe et al
• A Cortical Area Selective for Visual Processing of the Human Body; Paul E. Downing et al
• Triple Dissociation of Faces, Bodies, and Objects in Extrastriate Cortex; David Pitcher et al
• Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex; James V. Haxby et al
• Rectilinear Edge Selectivity Is Insufficient to Explain the Category Selectivity of the Parahippocampal Place Area; Peter B. Bryan et al
• Selective scene perception deficits in a case of topographical disorientation; Jessica Robin et al
• The cognitive map in humans: spatial navigation and beyond; Russell A Epstein et al
• From simple innate biases to complex visual concepts; Shimon Ullman et al
• Face perception in monkeys reared with no exposure to faces; Yoichi Sugita et al
• Functional neuroanatomy of intuitive physical inference; Jason Fischer et al
• Recruitment of an Area Involved in Eye Movements During Mental Arithmetic; André Knops et al
• Intonational speech prosody encoding in the human auditory cortex; C. Tang et al

World Model

• Recurrent World Models Facilitate Policy Evolution; David Ha et al
• TRANSFORMERS ARE SAMPLE-EFFICIENT WORLD MODELS; Vincent Micheli et al
• Language Models Meet World Models: Embodied Experiences Enhance Language Models; Jiannan Xiang et al
• Reasoning with Language Model is Planning with World Model; Shibo Hao et al
• Learning to Model the World with Language; Jessy Lin et al
• Learning Interactive Real-World Simulators; Mengjiao Yang et al
• Diffusion World Model; Zihan Ding et al
• Genie: Generative Interactive Environments; Jake Bruce et al
• Learning and Leveraging World Models in Visual Representation Learning; Quentin Garrido et al
• iVideoGPT: Interactive VideoGPTs are Scalable World Models; Jialong Wu et al

Resource

• LAVIS-A One-stop Library for Language-Vision Intelligence; https://github.com/salesforce/LAVIS
• MULTIVIZ: TOWARDS VISUALIZING AND UNDERSTANDING MULTIMODAL MODELS; Paul Pu Liang et al
• TorchScale - A Library for Transformers at (Any) Scale; Shuming Ma et al
• Video pretraining; https://zhuanlan.zhihu.com/p/515175476
• Towards Complex Reasoning: the Polaris of Large Language Models; Yao Fu
• Prompt Engineering; Lilian Weng
• Memory in human brains; https://qbi.uq.edu.au/brain-basics/memory
• Bloom's Taxonomy; https://cft.vanderbilt.edu/guides-sub-pages/blooms-taxonomy/#:~:text=Familiarly%20known%20as%20Bloom's%20Taxonomy,Analysis%2C%20Synthesis%2C%20and%20Evaluation.
• Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models’ Reasoning Performance; Yao Fu et al
• LLM Powered Autonomous Agents; Lilian Weng
• Retrieval-based Language Models and Applications; Tutorial; https://github.com/ACL2023-Retrieval-LM/ACL2023-Retrieval-LM.github.io
• Recent Advances in Vision Foundation Models; Tutorial; https://vlp-tutorial.github.io/
• LM-Polygraph: Uncertainty Estimation for Language Models; Ekaterina Fadeeva et al
• The Q* hypothesis: Tree-of-thoughts reasoning, process reward models, and supercharging synthetic data; Nathan Lambert
• Data-Juicer: A One-Stop Data Processing System for Large Language Models; Daoyuan Chen et al
• Designing, Evaluating, and Learning from Human-AI Interactions; Sherry Tongshuang Wu et al
• Reinforcement Learning from Human Feedback: Progress and Challenges; John Schulman
• Our approach to alignment research; OpenAI
• AI Alignment: A Comprehensive Survey; Jiaming Ji et al; https://alignmentsurvey.com/
• Alignment Workshop; https://www.alignment-workshop.com/nola-2023
• AI Alignment Research Overview; Jacob Steinhardt
• Proxy objectives in reinforcement learning from human feedback; John Schulman
• AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System; Zhiwei Liu et al
• Training great LLMs entirely from ground up in the wilderness as a startup; Yi Tay
• MiniCPM：揭示端侧大语言模型的无限潜力; 胡声鼎 et al
• Superalignment Research Directions; OpenAI; https://openai.notion.site/Research-directions-0df8dd8136004615b0936bf48eb6aeb8
• Llama 3 Opens the Second Chapter of the Game of Scale; Yao Fu
• RLHF Workflow: From Reward Modeling to Online RLHF; Hanze Dong et al
• TinyLLaVA Factory: A Modularized Codebase for Small-scale Large Multimodal Models; Junlong Jia et al