How LLMs Are Persuaded: A Few Attention Heads, Rerouted
Large language models can answer a factual question correctly, yet switch to an incorrect answer when exposed to persuasive context. Why this happens inside the model remains unclear. In this work, we study this question through causal interventions in controlled multiple-choice tasks and identify that persuasion mainly acts through a small set of mid-layer attention heads, which we call decision heads. These heads do not compute the answer themselves. Instead, they copy the option they attend to, so changing their attention can directly change the model's answer. We find that this attention is controlled by a one-dimensional option-routing feature in the option-token representations. Per...
| Authors | Xiangkun Sun, Lingkai Kong, Aoqi Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 May 2026 |
| Last updated at source | 28 September 2026 |
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