Ground Truth Generation for Multilingual Historical NLP using LLMs

Clovis Gladstone, Zhao Fang, Spencer Dean Stewart
11/18/2025
cs.CLcs.AI

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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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Cite this paper

@article{gladstone2025ground,
  title  = {Ground Truth Generation for Multilingual Historical NLP using LLMs},
  author = {Clovis Gladstone and Zhao Fang and Spencer Dean Stewart},
  year   = {2025},
  eprint = {2511.14688},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2511.14688v1}
}

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