Seventeenth-Century Spanish American Notary Records for Fine-Tuning Spanish Large Language Models

TL;DR

This paper introduces a historical Spanish notary record dataset from the 17th century, demonstrating its effectiveness in fine-tuning language models for various NLP tasks and outperforming existing models.

Contribution

The creation of a unique 17th-century Spanish notary record dataset for fine-tuning language models, enabling improved historical text analysis and NLP performance.

Findings

The dataset improves fine-tuning results for Spanish LLMs.

Fine-tuned models outperform pre-trained Spanish models.

The resource is publicly available for research.

Abstract

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.