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The paper introduces MORPHOGEN, a new benchmark dataset for evaluating gender-aware morphological generation in multilingual LLMs across French, Arabic, and Hindi. The core task, GENFORM, challenges models to rewrite first-person sentences in the opposite gender while preserving meaning and structure. Experiments on 15 multilingual LLMs (2B-70B) reveal significant gaps in their ability to handle morphological gender, highlighting the need for improved morphology-sensitive NLP.
Multilingual LLMs struggle to flip the gender of a sentence in languages like French, Arabic, and Hindi, revealing surprising limitations in their morphological understanding.
While multilingual large language models (LLMs) perform well on high-level tasks like translation and question answering, their ability to handle grammatical gender and morphological agreement remains underexplored. In morphologically rich languages, gender influences verb conjugation, pronouns, and even first-person constructions with explicit and implicit mentions of gender. We introduce MORPHOGEN, a morphologically grounded large-scale benchmark dataset for evaluating gender-aware generation in three typologically diverse grammatically gendered languages: French, Arabic, and Hindi. The core task, GENFORM, requires models to rewrite a first-person sentence in the opposite gender while preserving its meaning and structure. We construct a high-quality synthetic dataset spanning these three languages and benchmark 15 popular multilingual LLMs (2B-70B) on their ability to perform this transformation. Our results reveal significant gaps and interesting insights into how current models handle morphological gender. MORPHOGEN provides a focused diagnostic lens for gender-aware language modeling and lays the groundwork for future research on inclusive and morphology-sensitive NLP.