Reasoning Enhanced Missing Knowledge Retrieval Augmented Generation Framework for Domain Specific Question Answering

AbstractRetrieval Augmented Generation (RAG) framework mitigates hallucinations in Large Language Models (LLMs) by integrating external knowledge, yet faces two critical challenges: (1) the distribution gap between user queries and knowledge bases in a specific domain, and (2) incomplete coverage of required knowledge for complex queries. Existing solutions either require task-specific annotations or neglect inherent connections among query, context, and missing knowledge interactions. We propose a reasoning-based missing knowledge RAG framework that synergistically resolves both issues through Chain-of-Thought reasoning. By leveraging open-source LLMs, our method generates structured missing knowledge queries in a single inference pass while aligning query knowledge distributions, and integrates reasoning traces into answer generation. Experiments on open-domain medical and general question answering (QA) datasets demonstrate significant improvements in context recall and answer accuracy. Our approach achieves effective knowledge supplementation without additional training, offering enhanced interpretability and robustness for real-world QA applications.