CS Events

Qualifying Exam Understanding and Controlling Internal Representations in Large Language Models
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Speaker: Mingyu Jin

Bio

Location : CBIM 22

Committee: 

Professor Dimitris N. Metaxas

Assistant Professor Hongyi Wang

Assistant Professor Chengzhi Mao

Associate Professor Sudarsun Kannan

Event Type: Qualifying Exam

Abstract: I focus on understanding how large language models encode, organize, and utilize internal representations for knowledge acquisition, out-of-distribution generalization, and controllable generation. The exam is based on three recent works. The first studies how representation sparsity changes under distribution shift and shows that harder or more out-of-distribution inputs tend to induce sparser hidden representations, revealing a potential mechanistic signature of model uncertainty and failure. The second investigates how concepts emerge across layers in large language models, providing evidence that different layers capture knowledge and abstraction at different depths. The third explores how sparse representation spaces derived from sparse autoencoders can be used for reliable supervised steering, enabling more precise and interpretable control of model behavior. Together, these works advance a unified view that the geometry and sparsity of internal representations play a central role in both understanding and controlling large language models.

Organization: 

Contact  Professor Dimitris Metaxas

Zoom Link: https://rutgers.zoom.us/j/99389759582?pwd=0MZiKer0Bno8QMZG68KffEPNWZPNFz.1