YallaMorph is introduced, a large-scale benchmark for Arabic morphological generation covering verbs, nouns, adjectives, their cliticized forms, and invalid configurations over 600K benchmark entries, which shows that Arabic morphological generation remains difficult.
Abstract
Arabic morphology remains challenging for large language models, since fluent generation does not guarantee accurate morphosyntactic control. Existing Arabic evaluations mainly target downstream tasks and do not directly test controlled morphological generation from explicit lexical and feature-based input. We introduce YallaMorph, a large-scale benchmark for Arabic morphological generation covering verbs, nouns, adjectives, their cliticized forms, and invalid configurations. We evaluate multilingual and Arabic-oriented LLMs under diacritized and undiacritized settings over 600K benchmark entries. Results show that Arabic morphological generation remains difficult, especially for cliticized, unseen, and morphologically rare forms.
A baseline morphological analyzer is built by evaluating a wider range of supervised systems, including lexicon-lookup, Conditional Random Fields (CRFs), full fine-tuning of Arabic PLMs, and AraT5 configurations, to demonstrate severe limitations in current capabilities.
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The findings support a diagnostic rather than leaderboard interpretation: the apparent benefit of Arabic XTTS adaptation depends materially on the evaluation metric, checkpoint, and evaluator.
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