Record summary
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Risk profile
How this risk is described and categorized.
"In terms of inherent risk, LLMs could potentially reveal sensitive information from their utilized corpora for pre-training or fine-tuning, thereby raising issues of privacy leakage [37, 145, 226]. Meanwhile, it is well-known that LLMs may experi- ence hallucinations, resulting in the production of texts that are inaccurate and misleading [194]. Finally, since the values embedded in LLM-generated texts usually directly reflect the distribution of their training data, often sourced from the Internet, there exists a substantial risk that LLMs will overfit to a narrow set of human values or even amplify undesirable and unethical content, resulting in value mismatch."
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.