Agent-FLAN provides a method and dataset for fine-tuning Large Language Models (LLMs) to improve their agent capabilities, addressing issues of data entanglement, varying learning speeds, and hallucination prevalent in current agent tuning approaches. It targets researchers and developers aiming to enhance open-source LLMs for agentic tasks, offering a significant performance boost over prior methods.

How It Works

Agent-FLAN proposes a data-centric approach to agent tuning, involving careful decomposition and redesign of the training corpus. It incorporates comprehensively constructed negative samples to mitigate hallucination and improve agent reasoning. This method enables Llama2-7B to achieve state-of-the-art performance on agent evaluation datasets.

Quick Start & Requirements

• Models and datasets are available on Huggingface and OpenXLab.
• The project utilizes the Llama2-chat conversation format.
• Links to models, datasets, paper, and project page are provided.

Highlighted Details

• Outperforms prior agent-tuning approaches by 3.5% on agent evaluation datasets.
• Significantly alleviates hallucination issues based on an established evaluation benchmark.
• Consistently improves agent capability with model scaling while slightly enhancing general capabilities.
• Built with Lagent and T-Eval.

Maintenance & Community

• Paper available on ArXiv (March 2024).
• Dataset and model checkpoints released (March 2024).
• Citation details provided for academic use.

Licensing & Compatibility

• Released under the Apache 2.0 license.
• Compatible with commercial use and closed-source linking.

Limitations & Caveats

The README does not detail specific hardware requirements or setup time, nor does it mention any known limitations or ongoing development status beyond the initial release.