Abstract:

Large language models (LLMs) have demonstrated remarkable capabilities, but their high computational costs and reliance on extensive labeled data limit their practical deployment in resource-constrained settings. This talk explores strategies for efficiently adapting and leveraging smaller, more deployable models while minimizing reliance on human annotations.

I will discuss research on overcoming key challenges in model adaptation, including mitigating sensitivity to prompt variations, improving label efficiency through weak supervision, and optimizing sample selection in low-resource scenarios. Additionally, I will present ongoing efforts to narrow the performance gap between small and large LLMs through knowledge distillation. By integrating insights from model evaluation, data selection, and training optimizations, this talk highlights practical methodologies for achieving competitive performance while working within computational and budgetary constraints.

Bio:

I am a post-doctoral fellow at the University of Michigan (Michigan Institute for Data and Al in Society) under the guidance of Dr. David Jurgens and Dr. Lu Wang. I finished my Ph.D. in Computer Science from lowa State University. I have worked as a Research Assistant in the Data Mining and Knowledge Lab under my advisor, Dr. Qi Li. I have also worked as a Data Science intern at The Home Depot, Epsilon, and a Data Analytics intern at Delaware North. My research interests are in the domain of data mining, natural language processing, and machine learning. Through my research, I have contributed several key methods in top conferences like PAKDD’ 2025, SIAM’ 2025, ACL’ 2023, UAI’ 2023, SIGKDD’ 2022, ESEC/FSE’2021 and workshops like ICLR’ 2025, WWW’ 2025, PAKDD’ 2025, RANLP’2021.