4 papers · 1 filter
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…
SurrogateSHAP: Training-Free Contributor Attribution for Text-to-Image (T2I) Models
Mingyu Lu, Soham Gadgil, Chris Lin +2
As Text-to-Image (T2I) diffusion models are increasingly used in real-world creative workflows, a principled framework for valuing contributors who provide a collection of data is…
Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025
Emily Alsentzer, Marie-Laure Charpignon, Bill Chen +90
The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025…
Ensembling Sparse Autoencoders
Soham Gadgil, Chris Lin, Su-In Lee
Sparse autoencoders (SAEs) are used to decompose neural network activations into human-interpretable features. Typically, features learned by a single SAE are used for downstream a…