From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models

arXiv:2403.03893v1 Announce Type: new
Abstract: To date, toxicity mitigation in language models has almost entirely been focused on single-language settings. As language models embrace multilingual capabilities, it's crucial our safety measures keep pace. Recognizing this research gap, our approach expands the scope of conventional toxicity mitigation to address the complexities presented by multiple languages. In the absence of sufficient annotated datasets across languages, we employ translated data to evaluate and enhance our mitigation techniques. We also compare finetuning mitigation approaches …

arxiv cs.ai cs.cl language language models one to many toxicity type