Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization
Large Language Models (LLMs) exhibit strong implicit personalization ability, yet most existing approaches treat this behavior as a black box, relying on prompt engineering or fine tuning on user data. In this work, we adopt a mechanistic interpretability perspective and hypothesize the existence of a sparse set of Preference Heads, attention heads that encode user specific stylistic and topical preferences and exert a causal influence on generation. We introduce Differential Preference Steering (DPS), a training free framework that (1) identifies Preference Heads through causal masking analysis and (2) leverages them for controllable and interpretable personalization at inference time. D...
| Authors | Weixu Zhang, Ye Yuan, Changjiang Han et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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