Abstract: Complex question-answering tasks require models to efficiently retrieve relevant information from large-scale heterogeneous knowledge sources while supporting the generation of high-quality answers.However,existing retrieval-augmented generation methods face numerous challenges in knowledge retrieval,semantic relevance,and generation consistency:(1) the granularity and structured information of the knowledge retrieval module are insufficient;(2) there is a lack of contextual relevance in retrieval,limited ranking capability,and constrained generation quality;(3) generative models struggle to accurately integrate retrieved knowledge and produce contextually consistent answers.This paper proposes a novel framework,GraphRank-RAG,which combines graph-based retrieval-augmented generation with contextual ranking to address the issues mentioned.By introducing a graph-based retrieval mechanism,the framework captures deep semantic relationships within contexts,optimizing both the ranking process and answer generation.Experimental results demonstrate that the proposed method outperforms existing approaches on multiple open-domain question-answering datasets,achieving significant improvements in retrieval accuracy and generation quality.

Key words: Large language model, Retrieval augmented generation, Graph retrieval, Contextual rank, Retrieval technology