A Survey of Toxicity Detection and Mitigation Strategies for Multilingual Language Models

2026-06-24

AI Summary

This work addresses the inconsistent detection and mitigation of toxic content by multilingual large language models across diverse linguistic and cultural contexts. It presents the first systematic synthesis of research on multilingual toxicity handling, proposing a comprehensive framework that encompasses threat modeling, task formulation, detection strategies—such as cross-lingual encoders, translation pipelines, and representation probing—and mitigation approaches, including data filtering, alignment tuning, decoding controls, and multilingual safeguards. The study identifies core challenges such as uneven language coverage and culturally contingent definitions of harm, while highlighting critical issues like fragmented evaluation protocols and the unintended suppression of legitimate expression. By elucidating these dimensions, the paper establishes a theoretical foundation and practical roadmap for achieving cross-lingual safety alignment in multilingual language models.

Abstract

Large language models (LLMs) are increasingly deployed across languages, but their safety behavior remains uneven across linguistic and cultural contexts. This survey synthesizes work on toxicity detection and detoxification for multilingual LLMs. We first catalogue threat models that exploit language choice, translation pivots, code-switching, orthographic variation, multi-turn interaction, and post-deployment fine-tuning to weaken safety alignment. We then organize task formulations (toxic-to-neutral rewriting, toxicity classification, and toxic-generation evaluation), multilingual detection approaches (cross-lingual encoders, translation pipelines, representation-level probes, and LLM-based detectors), and mitigation strategies spanning data filtering, supervised and preference-based tuning, decoding-time steering, representation editing, and multilingual guardrails. Across these areas, we identify persistent challenges: uneven language coverage, culturally contingent definitions of harm, fragmented evaluation protocols, and the risk that detoxification suppresses legitimate dialectal or identity-related expression.

Problem

• toxicity detection
• multilingual language models
• safety alignment
• cultural context
• harm mitigation

Innovation

• multilingual toxicity detection
• safety alignment
• cross-lingual detoxification
• language model guardrails
• culturally contingent harm