总结Prompt&LLM论文,开源数据&模型,AIGC应用
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Updated
May 24, 2024
总结Prompt&LLM论文,开源数据&模型,AIGC应用
This repository highlights the LLMs reasoning capabilities of ✨ Mistral / LLaMA-3 / Phi-3 / Gemma / Flan-T5 / GPT-4o ✨ in Targeted Sentiment Analysis in Russian / Translated to English mass-media 📊
Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.
[EMNLP 2023, Findings] GRACE: Discriminator-Guided Chain-of-Thought Reasoning
✨✨Latest Papers and Datasets on Multimodal Large Language Models, and Their Evaluation.
The official GitHub page for the survey paper "A Survey of Large Language Models".
DriveLM: Driving with Graph Visual Question Answering
Reasoning in Large Language Models: Papers and Resources, including Chain-of-Thought, Instruction-Tuning and Multimodality.
Codes for ICML 2024 paper: "Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition"
Awesome deliberative prompting: How to ask LLMs to produce reliable reasoning and make reason-responsive decisions.
A framework for evaluating the effectiveness of chain-of-thought reasoning in language models.
This repository contains the code snippets used in "LLM Prompt Engineering For Developers"
An Easy-to-use Instruction Processing Framework for LLMs.
The official repo for “TextCoT: Zoom In for Enhanced Multimodal Text-Rich Image Understanding”.
Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models
ragTAG is a conversational AI script that creates a roundtable dialogue between user assigned characters with their own different objectives and perspectives.
Repository for Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions, ACL23
Speak It Out: Solving Symbol-Related Problems with Symbol-to-Language Conversion for Language Models
Official implementation for "Automatic Chain of Thought Prompting in Large Language Models" (stay tuned & more will be updated)
Official implementation of LoT paper: "Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic"
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