CDS PhD Seminar Series with Zhengyang Shan

Abstract: 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. Bio: 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).