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

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.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2607.23175 [cs.CL]

(or arXiv:2607.23175v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.23175 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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