LLMs show promise for recognizing entities in historical texts

Researchers have explored the use of large language models (LLMs) for Named Entity Recognition (NER) in historical texts, a task traditionally requiring extensive annotated data. Utilizing zero-shot and few-shot prompting techniques, the study demonstrated that LLMs can achieve promising performance on historical documents, despite falling short of fully supervised models. This approach offers a viable alternative for information extraction from low-resource historical corpora where traditional methods are impractical. AI

IMPACT LLMs offer a potential solution for information extraction from historical texts where traditional methods are infeasible.

RANK_REASON Academic paper exploring LLM application to a specific NLP task.

Read on arXiv cs.CL →

• HIPE-2022
• Large Language Models
• Named Entity Recognition
• Shibingfeng Zhang

• paper
• other

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