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
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% similarVerified大语言模型的幻觉问题源于其本质是基于统计概率的序列预测系统,生成的Token基于条件概率最大化而非真正的逻辑推理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
Cite This Claim
Stable URI
https://kongchang.com/claim/62187API
curl https://kongchang.com/api/v1/knowledge/claims/62187MCP
get_claim(id=62187)