arXiv Computation and Language
3d ago

Information-Theoretic Design of Sparse Autoencoder-Based Steering

The paper introduces an information‑theoretic framework for designing sparse autoencoder‑based steering interventions in large language models. It uses the Jensen–Shannon divergence between empirical feature‑firing distributions to quantify how much a state deviates from a target distribution and to construct steering vectors that guide hidden states toward desired firing patterns. Experiments demonstrate the method’s effectiveness in question‑mode generation, show structured downstream effects of local interventions, and apply the framework to safety‑related feature analysis.

By Hirofumi Morise
arXiv Machine Learning
Sep 10

Disentangling Steering Vectors

arXiv:2609.07037v1 Announce Type: new Abstract: Activation steering has emerged as a lightweight, inference-time approach to control the behavior of Large Language Models (LLMs). However, traditional...

By Takeru Hiramatsu, Kyohei Atarashi, Koh Takeuchi, Hisashi Kashima