Reasoning Retrieval-Augmented Generation Method Integrated with Dynamic Semantic Expansion
Wenxian Zeng, Xiangqi Liu · 2025
Aiming at the problems of insufficient accuracy of retrieved content, poor portability of hybrid retrieval, and semantic drift in professional domain question-answering systems using Retrieval-Augmented Generation (RAG) technology, this paper proposes a Reasoning Retrieval-Augmented Generation Method Integrated with Dynamic Semantic Expansion (DSE-RAG). This method enhances question-answering performance through a four-stage process: First, a dynamic semantic expansion model is utilized to expand the semantic diversity of user queries; second, a hybrid retrieval agent with reasoning capabilities is designed; then, a large language model-based text filter is introduced to accurately extract key text fragments and reduce input noise; finally, optimized contextual information and questions are combined to construct prompts, driving the generation model to output highly reliable answers. Experiments on the self-built Computer Knowledge Education dataset (CKE) and the public Medical dataset show that DSE-RAG improves the accuracy index of answering questions by 13% and 9% respectively compared with the traditional RAG method, and significantly outperforms mainstream retrieval-augmented methods in the retrieval recall rate index.