关键词: 检索增强生成, 知识图谱, 自然语言处理, 问答系统, 大语言模型, 垂直领域

Abstract: Existing Retrieval-Augmented Generation (RAG) question-answering systems in domain-specific applications face challenges such as a single retrieval path, insufficient coverage of users' implicit intents, and low-quality retrieved segments, resulting in inaccurate and incomplete answers. To address these issues, a dual-stage optimization approach, Pre-Answering and Retrieval Filtering (PARF), was proposed. By integrating domain knowledge graphs and prompt engineering techniques, Large Language Models (LLMs) were guided to generate preliminary answers, constructing a multi-directional retrieval path of "original query → preliminary answer → relevant segments" to expand the semantic space of the original query. Subsequently, the retrieved segments were scored and filtered using a Bidirectional Encoder Representations from Transformers (BERT) model to enhance relevance, enabling collaborative optimization between the retrieval and generation stages and improving the density of effective information. Experimental results showed that, compared to the Dense Passage Retrieval with LLM (DPR-LLM) baseline, the RAG question-answering system enhanced by PARF achieved improvements of 19.8 and 41.5 percentage points in F1 and Recall-Oriented Understudy for Gisting Evaluation (ROUGE-L) scores on a rail transportation QA dataset, and 16.1 and 17.6 percentage points on a medical QA dataset. The accuracy of effectiveness metrics increased by 10.2 and 8.8 percentage points, respectively. These findings demonstrate that PARF provides an effective solution for improving the accuracy and comprehensiveness of RAG-based question-answering systems in vertical domains.

Key words: Retrieval-Augmented Generation (RAG), knowledge graph, Natural Language Processing (NLP), question answering system, Large Language Model (LLM), vertical field