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arXiv · 2506.11602

Are LLMs Good Text Diacritizers? An Arabic and Yoruba Case Study

Hawau Olamide Toyin·Samar Mohamed Magdy·Hanan Aldarmaki
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Abstract

We investigate the effectiveness of large language models (LLMs) for text diacritization in two typologically distinct languages: Arabic and Yoruba. To enable a rigorous evaluation, we introduce a novel multilingual dataset MultiDiac, with diverse samples that capture a range of diacritic ambiguities. We evaluate 12 LLMs varying in size, accessibility, and language coverage, and benchmark them against $4$ specialized diacritization models. Additionally, we fine-tune four small open-source models using LoRA for Yoruba. Our results show that many off-the-shelf LLMs outperform specialized diacritization models, but smaller models suffer from hallucinations. We find that fine-tuning on a small dataset can help improve diacritization performance and reduce hallucinations for Yoruba.

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Hawau Olamide Toyin, Samar Mohamed Magdy, Hanan Aldarmaki. 2025-06-13. Are LLMs Good Text Diacritizers? An Arabic and Yoruba Case Study. https://arxiv.org/abs/2506.11602

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