Conversations in Galician: a Large Language Model for an Underrepresented Language

TL;DR

This paper introduces a Galician language adaptation of a large instruction dataset and fine-tunes a language model to support Galician, addressing the underrepresentation of low-resource languages in AI.

Contribution

It provides a novel Galician instruction dataset and demonstrates fine-tuning of LLaMA-7B for Galician, enhancing NLP capabilities for low-resource languages.

Findings

Galician dataset improves instruction adherence in models

Fine-tuned LLaMA-7B responds accurately in Galician

Knowledge of Portuguese aids in low-resource language modeling

Abstract

The recent proliferation of Large Conversation Language Models has highlighted the economic significance of widespread access to this type of AI technologies in the current information age. Nevertheless, prevailing models have primarily been trained on corpora consisting of documents written in popular languages. The dearth of such cutting-edge tools for low-resource languages further exacerbates their underrepresentation in the current economic landscape, thereby impacting their native speakers. This paper introduces two novel resources designed to enhance Natural Language Processing (NLP) for the Galician language. We present a Galician adaptation of the Alpaca dataset, comprising 52,000 instructions and demonstrations. This dataset proves invaluable for enhancing language models by fine-tuning them to more accurately adhere to provided instructions. Additionally, as a demonstration of the dataset utility, we fine-tuned LLaMA-7B to comprehend and respond in Galician, a language not originally supported by the model, by following the Alpaca format. This work contributes to the research on multilingual models tailored for low-resource settings, a crucial endeavor in ensuring the inclusion of all linguistic communities in the development of Large Language Models. Another noteworthy aspect of this research is the exploration of how knowledge of a closely related language, in this case, Portuguese, can assist in generating coherent text when training resources are scarce. Both the Galician Alpaca dataset and Cabuxa-7B are publicly accessible on our Huggingface Hub, and we have made the source code available to facilitate replication of this experiment and encourage further advancements for underrepresented languages.