用检索增强生成技术处理台湾历史档案，提升问答准确率。
A Preliminary Study of RAG for Taiwanese Historical Archives
- 将元数据提前融入检索流程，改善信息召回与生成质量。
- 在两个中文古籍数据集上验证，显著提升回答准确率。
- 适合历史文献数字化、数字人文研究者参考。
检索增强生成（RAG）在知识密集型任务中展现出巨大潜力，但针对台湾历史档案的研究仍很有限。本文首次系统考察了RAG管道在两个传统中文历史数据集——热兰遮城档案（Fort Zeelandia）和台湾省议会公报（Taiwan Provincial Council Gazette）——上的应用效果，以及对应的开放问答集。研究分析了查询特征与元数据融合策略对检索质量、答案生成及整体系统性能的影响。结果表明，早期整合元数据能显著提升检索与答案准确性；但系统仍存在生成幻觉、难以处理时间顺序或多跳历史查询等持续性挑战。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) has emerged as a promising approach for knowledge-intensive tasks. However, few studies have examined RAG for Taiwanese Historical Archives. In this paper, we present an initial study of a RAG pipeline applied to two historical Traditional Chinese datasets, Fort Zeelandia and the Taiwan Provincial Council Gazette, along with their corresponding open-ended query sets. We systematically investigate the effects of query characteristics and metadata integration strategies on retrieval quality, answer generation, and the performance of the overall system. The results show that early-stage metadata integration enhances both retrieval and answer accuracy while also revealing persistent challenges for RAG systems, including hallucinations during generation and difficulties in handling temporal or multi-hop historical queries.
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