CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs

• URL: http://arxiv.org/abs/2501.15067v1
• Date: Sat, 25 Jan 2025 04:18:08 GMT
• Title: CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs
• Authors: Yuntong Hu, Zhihan Lei, Zhongjie Dai, Allen Zhang, Abhinav Angirekula, Zheng Zhang, Liang Zhao,
• Abstract summary: Contextualized Graph Retrieval-Augmented Generation (CG-RAG) is a novel framework that integrates sparse and dense retrieval signals within graph structures. First, we propose a contextual graph representation for citation graphs, effectively capturing both explicit and implicit connections within and across documents. Second, we introduce Lexical-Semantic Graph Retrieval (LeSeGR), which seamlessly integrates sparse and dense retrieval signals with graph encoding. Third, we present a context-aware generation strategy that utilizes the retrieved graph-structured information to generate precise and contextually enriched responses.
• Score: 9.718354494802002
• License:
• Abstract: Research question answering requires accurate retrieval and contextual understanding of scientific literature. However, current Retrieval-Augmented Generation (RAG) methods often struggle to balance complex document relationships with precise information retrieval. In this paper, we introduce Contextualized Graph Retrieval-Augmented Generation (CG-RAG), a novel framework that integrates sparse and dense retrieval signals within graph structures to enhance retrieval efficiency and subsequently improve generation quality for research question answering. First, we propose a contextual graph representation for citation graphs, effectively capturing both explicit and implicit connections within and across documents. Next, we introduce Lexical-Semantic Graph Retrieval (LeSeGR), which seamlessly integrates sparse and dense retrieval signals with graph encoding. It bridges the gap between lexical precision and semantic understanding in citation graph retrieval, demonstrating generalizability to existing graph retrieval and hybrid retrieval methods. Finally, we present a context-aware generation strategy that utilizes the retrieved graph-structured information to generate precise and contextually enriched responses using large language models (LLMs). Extensive experiments on research question answering benchmarks across multiple domains demonstrate that our CG-RAG framework significantly outperforms RAG methods combined with various state-of-the-art retrieval approaches, delivering superior retrieval accuracy and generation quality.

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