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@xlang-ai

XLANG NLP Lab

Building language model agents that ground language instructions into code or actions executable in real-world environments

Welcome to the Executable Language Grounding (XLANG) Lab! We are part of the HKU NLP Group at the University of Hong Kong. XLang focuses on building language model agents that transform (“grounding”) language instructions into code or actions executable in real-world environments, including databases (data agent), web applications (plugins/web agent), and the physical world (robotic agent) etc,. It lies at the heart of language model agents or natural language interfaces that can interact with and learn from these real-world environments to facilitate human interaction with data analysis, web applications, and robotic instruction through conversation. Recent advances in XLang incorporate techniques such as LLM + external tools, code generation, semantic parsing, and dialog or interactive systems.

Pinned

  1. OSWorld OSWorld Public

    OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments

    Python 948 116

  2. OpenAgents OpenAgents Public

    OpenAgents: An Open Platform for Language Agents in the Wild

    Python 3.6k 381

  3. instructor-embedding instructor-embedding Public

    [ACL 2023] One Embedder, Any Task: Instruction-Finetuned Text Embeddings

    Python 1.7k 127

  4. text2reward text2reward Public

    [ICLR 2024] Code for the paper "Text2Reward: Automated Dense Reward Function Generation for Reinforcement Learning"

    Jupyter Notebook 95 3

  5. Binder Binder Public

    [ICLR 2023] Code for the paper "Binding Language Models in Symbolic Languages"

    Python 285 33

  6. DS-1000 DS-1000 Public

    [ICML 2023] Data and code release for the paper "DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation".

    Python 192 24

Repositories

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