2 papers
cs.LG2026
A Geometric View for Understanding Concept Learning and Neuron Interpretation in Sparse Autoencoders
Chenhao Zhang, Chris Lin, Su-In Lee
We propose a unified mathematical framework for a geometric understanding of concept learning and neuron interpretation in sparse autoencoders (SAEs). While SAEs improve interpreta…
cs.LG2026
Where to Steer: Input-Dependent Layer Selection for Steering Improves LLM Alignment
Soham Gadgil, Chris Lin, Su-In Lee
Steering vectors have emerged as a lightweight and effective approach for aligning large language models (LLMs) at inference time, enabling modulation over model behaviors by shift…