Personalised Classifier-Guided Decoding

Abstract

Toxic content is not universally defined: what one user finds offensive, another may find acceptable depending on cultural background, context, and purpose. Current LLM safety systems apply a single global toxicity threshold to every user, and adapting this behaviour after deployment is expensive. This paper asks whether a frozen LLM can instead be steered at inference time to follow individual users’ toxicity preferences across six toxicity dimensions, without retraining. A classifier-guided decoding framework driven by a per-user sensitivity vector is instantiated as three deployable strategies and evaluated on the PRISM preference dataset. All three strategies reduce per-user toxicity error by 15–21%, while preserving general-knowledge accuracy to within 0.7 pp of the unguided baseline. The central finding is directional steerability: the decoder responds to the shape of a user’s preference vector, producing category-specific reductions that align with per-user weights (median cosine similarity 0.845, p = 0.0097 above a permutation baseline). These results show that meaningful personalised toxicity control is achievable at deployment time, without retraining the model.