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arXiv · 2604.18914

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

Mehul Agarwal·Aditya Aggarwal·Arnav Goel·Medha Hira·Anubha Gupta
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Abstract

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.

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Mehul Agarwal, Aditya Aggarwal, Arnav Goel, Medha Hira, Anubha Gupta. 2026-04-20. MORPHOGEN: A Multilingual Benchmark for Evaluating Gender-Aware Morphological Generation. https://arxiv.org/abs/2604.18914

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