A Comparative Study of Multilingual Fine-tuning and Prompting for Automatic Text Readability Classification in Galician

Abstract

Despite advancements in automatic readability assessment, low-resource languages such as Galician remain under-explored. This study addresses this gap by presenting a comparative study of readability assessment techniques in Galician, including fine-tuning of encoder models as well as prompting strategies using large generative models. Due to the scarcity of native Galician resources, neural machine translation was employed to generate synthetic Galician data. The analysis begins with BERT-based monolingual models trained on the synthetic data. For multilingual models, the impact of using original versus translated data was compared in order to assess the effects of translation-based augmentation. Finally, several LLMs were evaluated using zero-shot and few-shot prompting methods. The results indicate that generative models are not yet competitive with encoder models tuned for text classification in Galician, and that data generated through machine translation improves the performance of monolingual models but has little effect on multilingual models.

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