MORPHOGEN: A Multilingual Benchmark for Evaluating Gender-Aware Morphological Generation

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 to gender. We thus introduce MORPHOGEN a morphologically grounded largescale benchmark dataset for evaluating genderaware generation in three typologically diverse grammatically gendered languages i.e. 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 highquality synthetic dataset spanning French, Arabic, and Hindi, and benchmark 15 popular multilingual LLMs (2B-70B) on their ability to perform this transformation. Our results reveal gaps and interesting insights into the handling of morphological gender in current models. MORPHOGEN offers a focused diagnostic lens for gender-aware language modeling and lays the groundwork for future research on inclusive and morphology-sensitive NLP.