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arXiv · 2511.14688

Ground Truth Generation for Multilingual Historical NLP using LLMs

Clovis Gladstone·Zhao Fang·Spencer Dean Stewart
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Abstract

Historical and low-resource NLP remains challenging due to limited annotated data and domain mismatches with modern, web-sourced corpora. This paper outlines our work in using large language models (LLMs) to create ground-truth annotations for historical French (16th-20th centuries) and Chinese (1900-1950) texts. By leveraging LLM-generated ground truth on a subset of our corpus, we were able to fine-tune spaCy to achieve significant gains on period-specific tests for part-of-speech (POS) annotations, lemmatization, and named entity recognition (NER). Our results underscore the importance of domain-specific models and demonstrate that even relatively limited amounts of synthetic data can improve NLP tools for under-resourced corpora in computational humanities research.

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Clovis Gladstone, Zhao Fang, Spencer Dean Stewart. 2025-11-18. Ground Truth Generation for Multilingual Historical NLP using LLMs. https://doi.org/10.63744/uwodsxrk90vn

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