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

ACL ARR 2025 May Submission2092 Authors

Abstract: Large language models (LLMs) often exhibit limited performance on domain-specific tasks due to the natural disproportionate representation of specialized information in their training data and the static nature of these datasets. Knowledge scarcity and temporal lag create knowledge gaps for domain applications. While post-training on domain datasets can embed knowledge into models, existing approaches have some limitations. Continual Pre-Training (CPT) treats all tokens in domain documents with equal importance, failing to prioritize critical knowledge points, while supervised fine-tuning (SFT) with question-answer pairs struggles to develop the coherent knowledge structures necessary for complex reasoning tasks. To address these challenges, we propose Reinforcement Learning from Augmented Generation (RLAG). Our approach iteratively cycles between sampling generations and optimizing the model through calculated rewards, effectively embedding critical and contextually coherent domain knowledge. We select generated outputs with the highest log probabilities as the sampling result, then compute three tailored reward metrics to guide the optimization process. To comprehensively evaluate domain expertise, we assess answer accuracy and the rationality of explanations generated for correctly answered questions. Experimental results across medical, legal, astronomy, and current events datasets demonstrate that our proposed method significantly outperforms baseline approaches. Our code and data will be made publicly available upon publication.

Paper Type: Long

Research Area: Machine Learning for NLP

Research Area Keywords: Dialogue and Interactive Systems, Language Modeling, Machine Learning for NLP

Contribution Types: NLP engineering experiment, Theory

Languages Studied: English

Keywords: Dialogue and Interactive Systems, Language Modeling, Machine Learning for NLP

Submission Number: 2092