A Survey of Toxicity Mitigation Strategies for Multilingual Language Models

Large language models (LLMs) are transforming natural language processing across diverse linguistic communities.However, they can reproduce and amplify toxic content, including hate speech, harassment, and bias, posing significant risks to multilingual applications.We provide the first comprehensive survey of the many detoxification methods specifically tailored to multilingual LLMs.First, we define toxicity its measurement, then we provide a brief review of monolingual mitigation strategies, including data filtering, style transfer, expert-based logit steering, retrieval augmentation, and alignment with human feedback.We then present an in-depth taxonomy of multilingual approaches spanning (1) training methods, (2) post-hoc editing and decoding strategies, (3) alignment and reinforcement-learning techniques, and (4) data-centric innovations, such as parallel detox corpora and synthetic data generation.Finally, we discuss open challenges in multilingual detoxification, including data scarcity, evaluation inconsistencies, cultural nuances and biases.Overall, we produce a needed overview of the state of multi-lingual toxicity detection and mitigation on which the community can ground to build globally safe and equitable LLMs.