Papers by Medha Hira

2 papers
MORPHOGEN: A Multilingual Benchmark for Evaluating Gender-Aware Morphological Generation (2026.acl-long)

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Challenge: In morphologically rich languages, gender influences verb conjugation, pronouns, and even first-person constructions with explicit and implicit mentions of gender.
Approach: They propose a morphologically grounded large-scale benchmark dataset for evaluating gender-aware generation in three typologically diverse grammatically gendered languages: French, Arabic, and Hindi.
Outcome: The proposed dataset compares 15 popular multilingual large language models on their ability to handle morphological gender and morphology agreement.
Mind the Gap: Multilingual Divide in LLM Bias Detection and Reasoning (2026.acl-srw)

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Challenge: Large Language Models (LLMs) are increasingly deployed in multilingual settings . but most bias evaluation remains English-centric and ignores how bias manifests within reasoning .
Approach: They evaluate large language models with supervised fine-tuning and preference optimization . they find that bias varies substantially across languages, with consistent degradation in non-English settings .
Outcome: The proposed model improves in English, Dutch, Spanish, and Turkish using the MBBQ benchmark.

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