•   When: Thursday, March 23, 2023 from 11:00 AM to 12:00 PM
•   Speakers: Sharon Levy
•   Location: ENGR 4201
•   Export to iCal

Abstract:

Large language models have advanced the state-of-the-art in natural language processing and achieved success in tasks such as summarization, question answering, and text classification. However, these models are trained on large-scale datasets, which may include harmful information. Studies have shown that as a result, the models exhibit social biases and generate misinformation after training. In this talk, I will discuss my work on analyzing and interpreting the risks of large language models across the areas of fairness, trustworthiness, and safety. I will first describe my research in the detection of dialect bias between African American English (AAE) vs. Standard American English (SAE). The second part will investigate the trustworthiness of models through the memorization and subsequent generation of conspiracy theories. The final part will discuss recent work in AI safety regarding text that may lead to physical harm. I will conclude my talk with discussions of future work in the area of Responsible AI.

Bio:

Sharon is a 5th-year Ph.D. candidate at the University of California, Santa Barbara, where she is advised by Professor William Wang. Her research interests lie in natural language processing, with a focus on Responsible AI. Sharon’s research spans the subareas of fairness, trustworthiness, and safety, with publications in ACL, EMNLP, WWW, and LREC. She has spent summers interning at AWS, Meta, and Pinterest. Sharon is a 2022 EECS Rising Star and a current recipient of the Amazon Alexa AI Fellowship for Responsible AI.