Verified70% confidenceFactExact time
LLMs are fundamentally probability distribution systems trained on massive amounts of text, where each output is a sampling of the next most likely token rather than retrieval and reasoning based on real-world knowledge.
4
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70%
Confidence
Long-term
Relevance
7/18/2026
First Seen
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Related Claims
VerifiedLLMs generate output token by token in an autoregressive manner, with each word's selection based on probability sampling from the current context.82% similarUnverifiedLLMs are essentially next-token prediction machines based on statistical patterns, and their output is the text sequence with the highest probability given the context.81% similarUnverifiedWhen generating text, an LLM does not maintain a queryable factual database, but predicts the next word one by one based on context; information that is high-frequency, coherent and repeatedly appearing in the training data is reproduced more stably, while low-frequency, contradictory or fragmentary facts are easily smoothed by the language model's fluency into seemingly complete narratives.79% similar
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