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.

Although the rise of Large Language Models (LLMs) in enterprise settings brings new opportunities and capabilities, it also brings challenges, such as the risk of generating inappropriate, biased, or misleading content that violates regulations and can have legal concerns. To alleviate this, we present "LLMGuard", a tool that monitors user interactions with an LLM application and flags content against specific behaviours or conversation topics. To do this robustly, LLMGuard employs an ensemble of detectors.