Unverified50% confidenceFactExact time
Large language models learn linguistic patterns rather than factual databases during training, and may therefore confidently generate non-existent case citations, incorrect statutory numbers, or fabricated contractual precedents.
1
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50%
Confidence
Long-term
Relevance
9/23/2026
First Seen
Sources
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Related Claims
UnverifiedLarge language models are based on historical training data and have knowledge limitations including the inability to access real-time data and the inability to reach non-public proprietary enterprise data.80% similarUnverifiedThe training corpora of large language models may include unpublished academic preprints and research notes, raising concerns about data contamination.77% similarVerifiedLarge language models have two inherent weaknesses: their knowledge is cut off at training time and they cannot obtain the latest information, and they can only generate text and cannot directly perform actions.77% similarUnverifiedTraining data for large language models includes both authoritative sources like Wikipedia and unreliable content like forum spam and deliberate fabrications76% similarUnverifiedRecursive language models fundamentally eliminate the failure mode of cheating by using information outside the subtask through context isolation, and can only learn truly recursive rules.75% similar
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