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@xlang-ai

XLANG NLP Lab

Developing embodied AI agents that empower users to use language to interact with digital and physical environments to carry out real-world tasks.

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.

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  1. OSWorld OSWorld Public

    [NeurIPS 2024] OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments

    Python 1.9k 233

  2. aguvis aguvis Public

    [ICML2025] Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction

    Python 304 19

  3. OpenAgents OpenAgents Public

    [COLM 2024] OpenAgents: An Open Platform for Language Agents in the Wild

    Python 4.3k 474

  4. instructor-embedding instructor-embedding Public

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

    Python 2k 150

  5. text2reward text2reward Public

    [ICLR 2024 Spotlight] Code for the paper "Text2Reward: Reward Shaping with Language Models for Reinforcement Learning"

    Jupyter Notebook 160 8

  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 244 26

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