This repository aims to provide an implementation of LM-Augmenter, a system designed to improve Large Language Models (LLMs) by integrating external knowledge and automated feedback. It is targeted at researchers and developers working on enhancing LLM factuality and robustness.

How It Works

The LM-Augmenter architecture, as described in the associated paper, likely involves a feedback loop where LLM outputs are validated against external knowledge sources. Automated feedback mechanisms then guide the LLM to correct factual inaccuracies or improve its responses, leading to more reliable and accurate text generation.

Quick Start & Requirements

• Primary install / run command: Not specified.
• Non-default prerequisites and dependencies: Not specified.
• Estimated setup time or resource footprint: Not specified.
• Links: Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback

Highlighted Details

• Based on the paper "Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback."
• Focuses on improving LLM factuality and robustness.
• Incorporates external knowledge and automated feedback mechanisms.

Maintenance & Community

• Notable contributors: Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, Jianfeng Gao.
• Community links (Discord/Slack, etc.) are not provided.

Licensing & Compatibility

• License type is not specified.
• Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The repository is described as "will provide soon an implementation," indicating it is not yet available. Details on setup, dependencies, and usage are absent.