Abstract:

Over the past decade, Natural Language Processing (NLP) has undergone a transformative journey, marked by profound changes, particularly in the development of Large Language Models (LLMs). While some applications of LLMs, such as dialogue agents, have become a common part of our daily lives, their underlying complexities can go unnoticed. This talk focuses on one key aspect of language comprehension-affects. Affective traits encompass factors such as emotions, humor, sarcasm, and moral values, all of which are essential for fully understanding what is being communicated. Our work examines these subtle elements, aiming to enhance the interpretative abilities of LLMs by deepening their understanding of these traits in language, contributing to more meaningful human-machine interactions.

Bio:

Shivani Kumar is a Postdoctoral Research Fellow at the School of Information at University of Michigan. Her current work focuses on developing culturally enriched and morally refined language models. She earned her PhD from IIIT Delhi, India, where she studied conversational Al and focused on how people express themselves in dialogue – including emotions, humor, sarcasm, and individual speaking styles. Shivani’s future research goals involve exploring how LLMs manage rhetorical elements in conversations, particularly in the domains of ethos, pathos, and logos.