Seventeenth-Century Spanish American Notary Records for Fine-Tuning Spanish Large Language Models
This provides a domain-specific resource for researchers and historians working with historical Spanish texts, though it is incremental as it applies existing fine-tuning methods to a new dataset.
The paper tackles the problem of customizing Spanish large language models for historical text analysis by introducing a collection of 160+ pages of seventeenth-century Spanish American notary records for fine-tuning. The result shows that fine-tuning with this dataset outperforms pre-trained Spanish models and ChatGPT-3.5/ChatGPT-4o in tasks like classification and masked language modeling.
Large language models have gained tremendous popularity in domains such as e-commerce, finance, healthcare, and education. Fine-tuning is a common approach to customize an LLM on a domain-specific dataset for a desired downstream task. In this paper, we present a valuable resource for fine-tuning LLMs developed for the Spanish language to perform a variety of tasks such as classification, masked language modeling, clustering, and others. Our resource is a collection of handwritten notary records from the seventeenth century obtained from the National Archives of Argentina. This collection contains a combination of original images and transcribed text (and metadata) of 160+ pages that were handwritten by two notaries, namely, Estenban Agreda de Vergara and Nicolas de Valdivia y Brisuela nearly 400 years ago. Through empirical evaluation, we demonstrate that our collection can be used to fine-tune Spanish LLMs for tasks such as classification and masked language modeling, and can outperform pre-trained Spanish models and ChatGPT-3.5/ChatGPT-4o. Our resource will be an invaluable resource for historical text analysis and is publicly available on GitHub.