Record summary
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Risk profile
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"Despite the rapid advancement of LLMs, hallucinations have emerged as one of the most vital concerns surrounding their use [54, 79, 86, 110, 242]. Hallucinations are often referred to as LLMs’ generating content that is nonfactual or unfaithful to the provided information [54, 79, 86, 242]. Therefore, hallucinations can be typically categorized into two main classes. The first is factuality hallucination, which describes the discrepancy between LLMs’ generated content and real-world facts. For example, if LLMs mistakenly take Charles Lindbergh as the first person who walked on the moon, it is a factuality hallucination [79]. The second is faithfulness hallucination, which describes the discrepancy between the generated content and the context provided by the user’s instructions or input, as well as the internal coherence of the generated content itself. For example, when LLMs perform the summarizing task, they occasionally tamper with some key information by mistakes, which is a faithfulness hallucination."
Suggested mitigations
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Source
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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.