Record summary
A quick snapshot of what this page covers.
Risk profile
How this risk is described and categorized.
"Toxicity in LLMs refers to the generation of harmful, offensive, or inappropriate content that can cause harm to individuals or groups. Both explicit and implicit forms of toxicity can be generated by LLMs, posing significant risks to society. Explicit toxicity encompasses a wide range of negative behaviors, including hate speech, harassment, cyberbullying, rude, and disrespectful comments, derogatory language, as well as allocational harms [2, 62, 90]. Besides, implicit toxicity does not involve overtly harmful language but may manifest through subtle forms such as sarcasm, irony, and humor, making it more difficult to detect [103, 213]."
Suggested mitigations
Defenses that may help with related attacks.
Source
Research source for this risk, when available.
Included resource
A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy
Original source
MIT AI Risk Repository
Open the public repository used for AI risk records and taxonomy fields.
