Research survey on reducing toxicity in language models

This technical overview examines methods for identifying and mitigating toxic content generated by large language models. The article explores definitions of toxicity in machine learning contexts, including rude, disrespectful, hateful, offensive, and abusive language. Multiple academic frameworks are reviewed to understand how toxicity can be categorized and measured across different linguistic expressions and cultural contexts. The survey discusses implementation challenges in content moderation, noting that toxicity determinations often prove subjective across different demographic groups. Various methodologies for diminishing unsafe content generation are examined to provide practical guidance for model deployment in real-world applications.