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Speaker Bio: Swabha Swayamdipta is an Assistant Professor of Computer Science and a Gabilan Assistant Professor at the University of Southern California. Her research interests lie in natural language processing and machine learning, with a primary focus on the evaluation of generative models of language, understanding the behavior of language models, and designing language technologies for societal good. At USC, Swabha leads the Data, Interpretability, Language, and Learning (DILL) Lab. She received her PhD from Carnegie Mellon University, followed by a postdoctoral position at the Allen Institute for AI. Her work has received awards at EMNLP, ICML, NeurIPS, and ACL. Her research is supported by awards from the National Science Foundation, the Allen Institute for AI, and a Rising Star Award from Intel Labs.
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Abstract: As large language models have become ubiquitous, it has proven increasingly challenging to enforce their accountability and safe deployment. In this talk, I will discuss the importance of ensuring the safety, responsibility, and accountability of Large Language Models (LLMs) throughout all stages of their development: pre-training, post-training evaluation, and deployment. First, I will present the idea of a unique LLM signature that can identify the model to ensure accountability. Next, I will present our recent work on reliably evaluating LLMs through our novel formulation of generation separability, and how this could lead to more reliable generation. Finally, I will present some ongoing work that demonstrates LLMs' ability to understand but not generate unsafe or untrustworthy content.
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Jan 10, 2024, 2:00 PM
289, Engineering VI