Binder addresses the challenge of grounding large language models (LLMs) in symbolic reasoning and execution environments, enabling them to interact with and leverage external tools and knowledge bases. It targets researchers and developers working on LLM agents, program synthesis, and knowledge-grounded AI, offering a method to improve LLM performance with minimal symbolic annotations.

How It Works

Binder employs a novel approach that binds LLMs to symbolic languages by fine-tuning them on a small set of input-output examples. This method allows LLMs to learn to generate symbolic programs that can be executed by an interpreter, effectively grounding their outputs in a verifiable and actionable format. The advantage lies in achieving state-of-the-art or comparable performance with significantly fewer annotations than traditional methods.

Quick Start & Requirements

• Install via conda env create -f py3.7binder.yaml and pip install records==0.5.3.
• Requires an OpenAI API key (e.g., for code-davinci-002, gpt-3.5-turbo, gpt-4-xxx).
• Setup involves activating the conda environment (conda activate binder) and placing the API key in key.txt.
• Official project page: https://github.com/xlang-ai/Binder
• Demo page: https://huggingface.co/spaces/xlang-ai/Binder

Highlighted Details

• Accepted to ICLR 2023 (Spotlight).
• Supports OpenAI chat series models (GPT-3.5, GPT-4).
• Achieves SOTA or comparable performance with ~10 program annotations.

Maintenance & Community

The project was accepted to ICLR 2023. The primary contributors are listed in the citation. Further updates are planned to refactor code and support more models.

Licensing & Compatibility

The repository does not explicitly state a license. The code is provided for research purposes, and compatibility with commercial or closed-source applications is not specified.

Limitations & Caveats

The project's reliance on OpenAI's API means it is subject to their rate limits and model availability. The README notes past issues with OpenAI's request limitations and the deprecation of Codex models, indicating potential instability or the need for ongoing adaptation to API changes.