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

What it is about

The paper asks whether general-purpose LLMs can restore diacritics, the Arabic short vowels and the Yoruba tone and vowel marks. The authors build MultiDiac, a small test set of fully diacritized sentences built around "base words" that have two to four valid readings, and add a small Yoruba training set. They run 12 LLMs under three prompt styles (language-agnostic, language-specific, and with the base word's gloss) and compare them with four specialised diacritizers. Large LLMs reach character error rates of roughly 2–30 percent and beat the specialised models on Arabic (CATT 10.3 percent, Shakkelha 11.5 percent); small models reach 20–60 percent and hallucinate, Jais going above 50 percent WER on Arabic by returning transliterations or translations. LoRA fine-tuning on the small Yoruba training set lowers error and hallucination for the small models.

Ideas

For Mova

I1 is a test for tools/hbo (unpointed mode, choice among the readings its tables give). I2 belongs in the same tool and in any teacher step that labels pointed forms for 1. Script and pointing. I3 is a prompt rule for that teacher step, not a change to the grammar. MultiDiac is NC data: it may be used for tests and measurements only, never built into a release.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.63317/5cm5exvnmd9r

@inproceedings{toyin2026are,
  title = {{Are LLMs Good Text Diacritizers? An Arabic and Yoruba Case Study}},
  author = {Toyin, Hawau Olamide and Magdy, Samar Mohamed and Aldarmaki, Hanan},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/5cm5exvnmd9r},
  url = {https://doi.org/10.63317/5cm5exvnmd9r}
}

References

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