mlsanityData to 7 Oct 2026

A Preliminary Study of RAG for Taiwanese Historical Archives

arXiv 2511.07445 ↗First posted 4 Nov 2025cs.CL cs.AIcited by 4

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

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.

Key facts. Cited by 4 papers in the map (data through 7 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.

Builds on

9 of 17 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Knowledge Retrieval Based on Generative AI8 Jan

Other references · 8

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  3. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  4. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,050
  5. 2023Can Large Language Models Be an Alternative to Human Evaluations?cited by 729
  6. 2024Benchmarking Retrieval-Augmented Generation for Medicinecited by 326
  7. 2024LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domaincited by 113
  8. 2025A Reasoning-Focused Legal Retrieval Benchmarkcited by 37

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026CodaRAG: Connecting the Dots with Associativity Inspired by Complementary Learningcited by 3
  2. 2026Only Ask What You Don't Know: Grounded Delta Planning for Efficient Multi-step RAGcited by 3
  3. 2026TRACE: Accountable Agentic Retrieval for Source Discovery in Digital Archivescited by 0
  4. 2026Bridging Static and Agentic RAG for Taiwanese Historical Question Answeringcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Improving Access to Historical Archives with Real-time RAG-based Systems ↗
  2. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  3. 2026Forgotten History or Test-of-Time? Retrospect and Prospect on RAG from an IR Perspective
  4. 2024Meta Knowledge for Retrieval Augmented Large Language Models
  5. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  6. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  7. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  8. 2025A Proposed Large Language Model-Based Smart Search for Archive System