用知识图谱增强大模型，让历史文本分析更准更省力。
Research on Graph-Retrieval Augmented Generation Based on Historical Text Knowledge Graphs
- 结合思维链与自指导生成，少人工标注构建历史人物关系数据集。
- 引入图谱与检索协同机制，使模型推理更贴近历史事实，F1提升至0.68。
- 适合历史文献研究者和低资源古籍处理场景，减少幻觉、提升可解释性。
本文针对通用大模型在计算人文与AIGC背景下历史文本分析中的领域知识缺失问题，提出Graph RAG框架。该框架融合思维链提示、自指导生成与过程监督，以最小人工标注构建首个《四史》人物关系数据集，支持自动化历史知识提取，降低人力成本。在图谱增强生成阶段，引入知识图谱与检索增强生成的协同机制，提升通用模型对历史知识的对齐能力。实验表明，使用简体中文输入与思维链提示的专用模型Xunzi-Qwen1.5-14B在关系抽取任务中表现最优，F1达0.68。集成GraphRAG的DeepSeek模型在开放域C-CLUE关系抽取数据集上F1提升11%（0.08→0.19），超越Xunzi-Qwen1.5-14B的0.12，有效缓解幻觉现象并增强可解释性。该框架为经典文本知识提取提供低资源解决方案，推动历史知识服务与人文学科研究发展。
原文摘要 · Abstract (English)
This article addresses domain knowledge gaps in general large language models for historical text analysis in the context of computational humanities and AIGC technology. We propose the Graph RAG framework, combining chain-of-thought prompting, self-instruction generation, and process supervision to create a The First Four Histories character relationship dataset with minimal manual annotation. This dataset supports automated historical knowledge extraction, reducing labor costs. In the graph-augmented generation phase, we introduce a collaborative mechanism between knowledge graphs and retrieval-augmented generation, improving the alignment of general models with historical knowledge. Experiments show that the domain-specific model Xunzi-Qwen1.5-14B, with Simplified Chinese input and chain-of-thought prompting, achieves optimal performance in relation extraction (F1 = 0.68). The DeepSeek model integrated with GraphRAG improves F1 by 11% (0.08-0.19) on the open-domain C-CLUE relation extraction dataset, surpassing the F1 value of Xunzi-Qwen1.5-14B (0.12), effectively alleviating hallucinations phenomenon, and improving interpretability. This framework offers a low-resource solution for classical text knowledge extraction, advancing historical knowledge services and humanities research.
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