Zoom Info: https://illinois.zoom.us/j/3499616081?pwd=YnFDdmYrNzc1UXhaSkh5OGpNb2w3UT09
Abstract:
As machine learning (ML) models continue to scale in size and capability, they expand the surface area for safety and privacy risks, raising concerns about model trustworthiness and responsible data use. My research uncovers and mitigates these risks. In this presentation, I will focus on the three cornerstones of trustworthy foundation models and agents: safety, privacy, and generalization. For safety, I will introduce our comprehensive benchmarks designed to evaluate trustworthiness risks in Large Language Models (LLMs) and LLM-based code agents. For privacy, I will present a solution for protecting data privacy with a synthetic text generation algorithm under differential privacy guarantees. The algorithm requires only LLMs inference API access without model training, enabling efficient safe text sharing. For generalization, I will introduce our study on the interplay between the memorization and generalization of LLMs in logical reasoning during the supervised fine-tuning (SFT) stage. Finally, I will conclude with my future research plan for assessing and improving trustworthiness in foundation model-powered ML systems.
Bio:
Chulin Xie is a PhD candidate in Computer Science at the University of Illinois Urbana-Champaign, advised by Professor Bo Li. Her research focuses on trustworthy machine learning, addressing the safety, privacy and generalization risks of Foundation Models, agents, and federated (distributed) learning. Her work was recognized by an Outstanding Paper Award at NeurIPS 2023, a Best Research Paper Finalist at VLDB 2024, and press coverage like The Verge and TechCrunch. She was a recipient of 2024 Rising Star in Machine Learning and 2024 IBM PhD Fellowship.