Leveraging External Knowledge for Historical Document Restoration via Retrieval-Augmented Large Language Models
Historical documents are often illegible due to damage. Existing restoration methods using masked language modeling fail with named entities needing external knowledge. A new framework ARI combines LLMs with retrieval-augmented generation (RAG) to infer proper nouns. Experiments on Korean historical texts show significant improvements, validated by expert assessments.
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[Submitted on 24 Jul 2026]
Title:Leveraging External Knowledge for Historical Document Restoration via Retrieval-Augmented Large Language Models
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Abstract:Historical documents act as invaluable knowledge archives but often suffer from illegibility due to physical deterioration and damage. While existing restoration methods based on masked language modeling effectively utilize local context, they struggle to restore named entities that require external historical knowledge. To address this limitation, we introduce a novel framework for historical document restoration that leverages large language models with retrieval-augmented generation (RAG). By combining the implicit knowledge of pre-trained LLMs with explicitly retrieved external context, our model ARI effectively mitigates the challenge of inferring context-dependent proper nouns. Extensive experiments on Korean historical documents demonstrate that our approach significantly outperforms baselines, achieving substantial gains in restoring both general characters and named entities. Furthermore, comprehensive evaluations including expert assessments confirm that ARI serves as a practical tool for domain experts, promising to accelerate the analysis of historical records.
Comments: Accepted to Findings of ACL 2026
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2607.21936 [cs.CL]
(or arXiv:2607.21936v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.21936
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Findings of the Association for Computational Linguistics: ACL 2026, pages 43290-43304, 2026
Related DOI:
https://doi.org/10.18653/v1/2026.findings-acl.2148 https://doi.org/10.18653/v1/2026.findings-acl.2148
DOI(s) linking to related resources
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From: Gabeen Kim [view email] [v1] Fri, 24 Jul 2026 03:11:25 UTC (1,339 KB)
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