Record summary
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Risk profile
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Significant concerns are raised about LLMs inadvertently generating false or misleading information, as well as erroneous code. Papers not only critically analyze various types of reasoning errors in LLMs but also examine risks associated with specific types of misinformation, such as medical hallucinations. Given the propensity of LLMs to produce flawed outputs accompanied by overconfident rationales and fabricated references, many sources stress the necessity of manually validating and fact-checking the outputs of these models.
Suggested mitigations
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Source
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Included resource
Mapping the Ethics of Generative AI: A Comprehensive Scoping Review
Original source
MIT AI Risk Repository
Open the public repository used for AI risk records and taxonomy fields.
