# A Preliminary Study of RAG for Taiwanese Historical Archives

arXiv: https://arxiv.org/abs/2511.07445 · HTML: https://mlsanity.com/paper/2511.07445/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 9 of 9)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Can Large Language Models Be an Alternative to Human Evaluations?](https://mlsanity.com/paper/2305.01937/) (2023)
- [Benchmarking Retrieval-Augmented Generation for Medicine](https://mlsanity.com/paper/2402.13178/) (2024)
- [LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain](https://mlsanity.com/paper/2408.10343/) (2024)
- [A Reasoning-Focused Legal Retrieval Benchmark](https://mlsanity.com/paper/2505.03970/) (2025)
- [Knowledge Retrieval Based on Generative AI](https://mlsanity.com/paper/2501.04635/) (2025)

## Built on by (most cited first, 4 of 4)

- [CodaRAG: Connecting the Dots with Associativity Inspired by Complementary Learning](https://mlsanity.com/paper/2604.10426/) (2026)
- [Only Ask What You Don't Know: Grounded Delta Planning for Efficient Multi-step RAG](https://mlsanity.com/paper/2606.22681/) (2026)
- [TRACE: Accountable Agentic Retrieval for Source Discovery in Digital Archives](https://mlsanity.com/paper/2609.19897/) (2026)
- [Bridging Static and Agentic RAG for Taiwanese Historical Question Answering](https://mlsanity.com/paper/2609.23056/) (2026)

## Abstract (arXiv)

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.