Unverified50% confidenceFactExact time
Large language models carry a hallucination risk when generating parameters, such as fabricating non-existent user segment IDs.
1
Sources
50%
Confidence
Long-term
Relevance
10/8/2026
First Seen
Sources
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Related Claims
UnverifiedLarge language models may generate content that appears credible but is completely wrong (hallucinations), thereby misleading users' decisions.78% similarUnverifiedLarge language models still face hallucination problems in information extraction tasks, potentially completing information not explicitly mentioned in conversations and causing inaccurate CRM data.71% similarUnverified欺骗是模型在内部已表征正确信息的情况下仍输出误导性内容，是系统性偏离而非能力不足70% similarUnverifiedWhen semantically similar but inaccurate content is retrieved, the model may output incorrect recollections with high confidence, with no warning mechanism at the system level — this is the false memory problem.69% similarUnverifiedIn structured query scenarios, hallucinations can appear in the form of numbers that seem plausible but are actually incorrect, and once they enter a data pipeline they propagate silently with extremely high investigation costs67% similar
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