Full Professor, Computer Science Department, Stanford University
3 papers at NeurIPS 2025
We analyze the effective depth of LLMs and find that they are unlikely to compose subresults, and deeper models spread out the same type of computation as the shallow ones.
We propose Reference-free Preference Steering (RePS), a bidirectional preference-optimization objective that jointly does concept steering and suppression.
A benchmark dataset of oil and gas video ads
