Reducing Toxicity in Language Models
Read OriginalThis technical article examines the problem of toxicity, bias, and unsafe content in large pretrained language models. It discusses the difficulties in defining and categorizing toxic language, reviews existing taxonomies like the OLID dataset hierarchy, and introduces methodologies for mitigating these issues to enable safer real-world deployment of NLP models.
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