Unverified50% confidenceFactExact time
Large language models generate text by maximizing the statistical probability of the next word, so they tend to choose the most frequent, safest, and least offensive wording appearing in the training corpus, which is the source of ritual greetings, excessive politeness, and filler.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Related Entities
Related Claims
Unverified当训练数据中某些文本出现频率极高时,大语言模型可能产生记忆化(Memorization)现象,能够近乎逐字输出特定段落75% similarUnverifiedWhen generating text, language models default to selecting word sequences with higher conditional probability.75% similarVerifiedLarge language models are trained on vast corpora of text data and learn to predict the probability distribution of the next word74% similarVerifiedThe longer the text, the higher the confidence of watermark detection; short texts have high statistical noise and a greater risk of false positives, while detection accuracy is more reliable for texts of several hundred tokens or more.74% similarUnverified大语言模型的用量计费通常基于token(词元)消耗,而非简单的对话次数74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1043041API
curl https://kongchang.com/api/v1/knowledge/claims/1043041MCP
get_claim(id=1043041)