Abstract

Abstract:

Contextual understanding is central to the performance of Large Language Models, yet it remains one of their most persistent challenges. In this talk, I will explore how LLMs navigate (and often fail to navigate) the complexities of context, illustrated through two case studies: idiomaticity detection and fairness in identifying dehumanizing language. Idiomaticity detection requires models to distinguish between literal and figurative meanings, relying on nuanced contextual reasoning. Our experiments with a controlled contrastive dataset reveal that even state-of-the-art models struggle to resolve such ambiguities, often failing to attend to subtle contextual cues. In the domain of fairness, we investigate how LLMs identify dehumanizing language, a critical task where understanding social and linguistic contexts is key. Biases in the models’ sensitivity to different protected attributes highlight an incomplete grasp of the contexts in which dehumanization occurs, underscoring ethical implications for their deployment in sensitive applications. By reading between the lines, this talk shows the limitations of LLMs in contextual understanding across linguistic and ethical dimensions

Bio: Dr. Nafise Sadat Moosavi is a Lecturer in Natural Language Processing at the University of Sheffield, specializing in robust, fair, and trustworthy AI. Her research focuses on the nuanced challenges of large language models, including reasoning, efficiency, robustness, and fairness. Dr. Moosavi earned her Ph.D. in Computational Linguistics from the University of Heidelberg. She has held senior roles in top NLP conferences, including serving as Senior Area Chair for ACL , NAACL, and EMNLP , and has been the co-initiator and co-organizer of the SustaiNLP workshop series. As Deputy Director of Equality, Diversity, and Inclusion, she leads initiatives to foster equity and inclusivity in academia.