Verified70% confidenceFactExact time
When generating each token, large language models perform probabilistic sampling over all possible next words in the vocabulary, rather than deterministically selecting a unique answer.
4
Sources
70%
Confidence
Long-term
Relevance
7/15/2026
First Seen
Sources
Related Claims
VerifiedThe essence of large language models is learning the conditional probability distribution of what the next word is given the preceding text85% similarUnverified语言建模将一段文本的联合概率分解为每个词在其前文条件下的条件概率之积83% similarUnverified大语言模型是概率性、随机性的，它根据之前出现的词元来预测下一个词元83% similarUnverifiedLarge language models generate content based on predicting the next most likely token word by word, rather than retrieving answers from a factual database.82% similarUnverifiedLarge language models generate text in an autoregressive manner, where the production of each token depends on the context formed by all preceding tokens, following the chain rule of probability.80% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/510739API
curl https://kongchang.com/api/v1/knowledge/claims/510739MCP
get_claim(id=510739)