Unverified50% confidenceTradeoffExact time
Large language models carry a hallucination risk, and the accuracy and verifiability of their analytical conclusions on business data is the central question in determining whether such tools can be used for critical decision-making.
1
Sources
50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
Related Entities
Related Claims
Verified大型语言模型幻觉的根本原因在于其生成机制，模型本质上是概率预测系统，基于训练数据的统计模式预测下一个最可能的token77% 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.76% similarUnverifiedThe confidence output of large language models is fundamentally a product of probability distributions rather than a genuine judgment of factual truth or falsehood, and may express high confidence in incorrect statements.74% 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.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/906731API
curl https://kongchang.com/api/v1/knowledge/claims/906731MCP
get_claim(id=906731)