用核心概念构建学术图谱，提升大模型科研问答准确率
CE-GOCD: Central Entity-Guided Graph Optimization for Community Detection to Augment LLM Scientific Question Answering
- 以论文标题为枢纽，提取相关知识子图
- 通过图剪枝与补全增强语义发现能力
- 社区检测聚类主题相近论文，助力精准回答
大型语言模型（LLMs）在科研论文问答中应用日益广泛。现有检索增强方法多依赖孤立文本片段或概念，忽视论文间的深层语义关联，影响大模型对科学文献的理解，导致回答不全面且缺乏针对性。为此，我们提出中心实体引导的图优化社区发现方法（CE-GOCD），通过显式建模学术知识图谱中的语义子结构来增强大模型的科研问答能力。该方法包括：(1) 以论文标题作为中心实体进行目标子图检索；(2) 通过子图剪枝与补全提升隐含语义发现效果；(3) 应用社区检测算法提取具有共同主题的论文群组。我们在三个基于NLP的文献问答数据集上评估该方法，结果表明其优于其他检索增强基线模型，验证了框架的有效性。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly used for question answering over scientific research papers. Existing retrieval augmentation methods often rely on isolated text chunks or concepts, but overlook deeper semantic connections between papers. This impairs the LLM's comprehension of scientific literature, hindering the comprehensiveness and specificity of its responses. To address this, we propose Central Entity-Guided Graph Optimization for Community Detection (CE-GOCD), a method that augments LLMs' scientific question answering by explicitly modeling and leveraging semantic substructures within academic knowledge graphs. Our approach operates by: (1) leveraging paper titles as central entities for targeted subgraph retrieval, (2) enhancing implicit semantic discovery via subgraph pruning and completion, and (3) applying community detection to distill coherent paper groups with shared themes. We evaluated the proposed method on three NLP literature-based question-answering datasets, and the results demonstrate its superiority over other retrieval-augmented baseline approaches, confirming the effectiveness of our framework.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。