Abstract
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.
Abstract (translated)
这篇文章探讨了通用大型语言模型在计算人文和AIGC技术背景下进行历史文本分析时的知识领域差距。我们提出了Graph RAG框架，结合链式思维提示、自指令生成和过程监督来创建《史记》四书人物关系数据集，并且仅需少量的手动标注。这个数据集支持自动化的历史知识提取，从而降低了劳动成本。在图增强的生成阶段，我们引入了知识图谱与检索增强生成之间的协作机制，以改善通用模型与历史知识的一致性。 实验结果显示，在使用简体中文输入和链式思维提示的情况下，特定领域的模型Xunzi-Qwen1.5-14B在关系提取任务上取得了最优性能（F1 = 0.68）。DeepSeek模型与GraphRAG集成后，在开放领域C-CLUE关系抽取数据集上的表现提升了11%（从0.08到0.19），超过了Xunzi-Qwen1.5-14B的F1值（0.12），有效减轻了幻觉现象，并提高了模型解释性。 此框架为经典文本知识提取提供了一种低资源解决方案，促进了历史知识服务和人文研究的发展。
URL
https://arxiv.org/abs/2506.15241
