arXiv AI By Vincent Siu, Glenn Grant-Richards, Vlad Pavlovich, Yizhou Sun, Dawn Song, Chenguang Wang

Component and Dimension Sparsity in Transformer Refusal Mechanisms

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arXiv Computation and Language
Sep 16

There Is More to Refusal in Large Language Models than a Single Direction

The paper challenges the notion that refusal in large language models is governed by a single direction in activation space. It demonstrates that different refusal and non‑compliance categories map to distinct geometric directions, yet steering along any of these directions yields similar refusal–over‑refusal trade‑offs, acting as a shared one‑dimensional control knob. Using sparse autoencoders, the authors reveal a structured internal representation of refusal, comprising a reusable core of shared latents and style‑ or domain‑specific latents, and show that linear interventions collapse this structure into uniform behavioral control.

By Faaiz Joad, Majd Hawasly, Sabri Boughorbel, Nadir Durrani, Husrev Taha Sencar
arXiv Computation and Language
Aug 25

The Communication Map of a Transformer

The paper introduces the Communication Map, a method that charts every potential communication channel in a transformer model using only its weights. It generalizes previous coupling metrics into a single coefficient covering all 18 connection classes, revealing that 70‑89% of head pairs are non‑randomly oriented and identifying strong or avoiding couplings. The authors demonstrate the map’s utility by recovering known induction circuits and uncovering a two‑dimensional stream subspace whose removal eliminates induction capabilities across several models.

By Richard Zhe Wang