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Embedding Domain Knowledge for Large Language Models via Reinforcement Learning from Augmented Generation

Created by
  • Haebom

Author

Chaojun Nie, Jun Zhou, Guanxiang Wang, Shisong Wu, Zichen Wang

Outline

This paper describes "Reinforcement Learning from Augmented Generation (RLAG)," a reinforcement learning-based methodology proposed to address the limited performance of large-scale language models (LLMs) for domain-specific knowledge. RLAG iteratively optimizes the model based on generated outputs, effectively embedding important domain knowledge. It selects the output with the highest log probability and uses three custom reward metrics to guide the optimization process. Experiments on medical, legal, astronomy, and current affairs datasets demonstrate that RLAG outperforms existing methods.

Takeaways, Limitations

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Takeaways:
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RLAG presents an effective methodology to improve the performance of LLM for specific domain knowledge.
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The proposed RLAG contributes to developing a consistent knowledge structure required for complex reasoning tasks.
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We demonstrate the generalizability of our methodology through experiments on various domain datasets.
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Make code and data open to facilitate the reproduction and extension of research.
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Limitations:
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Information about the specific model architecture, hyperparameter settings, and computational cost is lacking.
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It is unclear how RLAG's performance compares to other state-of-the-art models.
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There is no guarantee that RLAG can solve all knowledge gap problems.
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