Gender Inclusivity Fairness Index (GIFI): A Multilevel Framework for Evaluating Gender Diversity in Large Language Models
About
We introduce a comprehensive framework for assessing gender fairness in large language models (LLMs), particularly in their treatment of both binary and non-binary genders. Existing research has largely focused on binary gender distinctions, neglecting the inclusivity of non-binary identities. To address this, the authors propose a novel metric that evaluates LLMs across seven dimensions. The study conducts extensive evaluations on 15 popular LLMs, revealing significant discrepancies in their ability to fairly represent diverse gender identities.
Speaker
Zhengyang Shan
Zhengyang is a third-year PhD student at Boston University's Faculty of Computing and Data Sciences. Her research interests lie in the evaluation, interpretability, and fairness of Large Language Models (LLMs).