Leveraging External Knowledge for Historical Document Restoration via Retrieval-Augmented Large Language Models
ARI uses retrieval-augmented LLMs to restore damaged Korean historical documents, especially proper names that local context misses.
The paper says masked-language approaches struggle when the missing text depends on outside historical knowledge. Its framework combines pretrained LLM knowledge with retrieved external context to infer context-dependent named entities. Experiments on Korean historical documents report significant gains over baselines for both general characters and named entities. Expert evaluations frame ARI as a practical aid for domain specialists working through damaged records. HF Daily Papers' note
The paper says masked-language approaches struggle when the missing text depends on outside historical knowledge. Its framework combines pretrained LLM knowledge with retrieved external context to infer context-dependent named entities. Experiments on Korean historical documents report significant gains over baselines for both general characters and named entities. Expert evaluations frame ARI as a practical aid for domain specialists working through damaged records. HF Daily Papers' note
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