From Stories to Features: Scaling Concept Vectors for AI Alignment & Interpretability.

arXiv:2610.24556v1 Announce Type: cross Abstract: We study feature vectors: directions in a language model's activation space that correspond to human-interpretable concepts, such as honesty or deception. Existing unsupervised methods like sparse autoencoders