3 papers
cs.LG2026
Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs
Robert Graham, Edward Stevinson, Yariv Barsheshat
Finetuning language models on small, curated datasets is standard practice for adapting them to specific policies or domains. We show that finetuning on narrow, factually-defensibl…
cs.CV2025
Steering CLIP's vision transformer with sparse autoencoders
Sonia Joseph, Praneet Suresh, Ethan Goldfarb +6
While vision models are highly capable, their internal mechanisms remain poorly understood -- a challenge which sparse autoencoders (SAEs) have helped address in language, but whic…
cs.CV2025
Prisma: An Open Source Toolkit for Mechanistic Interpretability in Vision and Video
Sonia Joseph, Praneet Suresh, Lorenz Hufe +7
Robust tooling and publicly available pre-trained models have helped drive recent advances in mechanistic interpretability for language models. However, similar progress in vision…