Beyond a Global Norm: Personalizing Toxicity Sensitivity in Language Models Without Retraining

Rares A. C. Diaconescu; Iulia Slanina; Alina Florea; Andrei B. Trache; Miruna E. Coroi; Anne Arzberger; Jie Yang; Enrico Liscio

arXiv · 2607.23175

Beyond a Global Norm: Personalizing Toxicity Sensitivity in Language Models Without Retraining

Rares A. C. Diaconescu·Iulia Slanina·Alina Florea·Andrei B. Trache·Miruna E. Coroi·Anne Arzberger·Jie Yang·Enrico Liscio

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Abstract

Reducing toxicity is often framed as a global alignment problem, yet perceptions of harmful language are subjective and context-dependent. We present the first comparative evaluation of training-free methods for aligning language generation to user-specific toxicity sensitivities across three inference-time intervention stages: pre-decoding (prompt conditioning and rewriting), in-decoding (token, logit, and representation steering), and post-decoding (candidate re-ranking). Evaluated against toxicity sensitivity targets derived from the PRISM dataset, all methods reduce alignment error by 28-47%. However, the results reveal a fundamental trade-off between alignment effectiveness, personalization, and general language quality, showing how toxicity sensitivity alignment is an inherently multi-objective problem.

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Rares A. C. Diaconescu, Iulia Slanina, Alina Florea, Andrei B. Trache, Miruna E. Coroi, Anne Arzberger, Jie Yang, Enrico Liscio. 2026-07-25. Beyond a Global Norm: Personalizing Toxicity Sensitivity in Language Models Without Retraining. https://arxiv.org/abs/2607.23175

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