Expired70% confidenceFactExact time
Large language models are fundamentally estimators of the conditional probability distribution P(output|input), with the goal of generating "statistically plausible" text rather than guaranteeing factual accuracy.
4
Sources
70%
Confidence
Medium-term (~90 days)
Relevance
7/4/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 10/2/2026(expired)
Sources
AI Agent实战指南：内容生成到企业级应用全解析
bilibili数据拆解室7/2/2026
Related Claims
VerifiedThe essence of large language models is learning the conditional probability distribution of what the next word is given the preceding text79% similarVerifiedThe hallucination problem in large language models stems from their nature as statistical probability-based sequential prediction systems, where generated tokens are based on conditional probability maximization rather than genuine logical reasoning.78% similarUnverifiedLarge language models can approximately estimate decision confidence by analyzing token probability distributions, explicitly outputting self-evaluation scores, or observing answer consistency across multiple samplings.77% similar
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