Verified70% confidenceFactExact time
Modern large language models are based on the Transformer architecture and generate text by predicting the probability distribution of the next token, with the training objective of minimizing cross-entropy loss
4
Sources
70%
Confidence
Long-term
Relevance
7/14/2026
First Seen
Sources
AI提升科研效率，却让科学发现走向同质化
hackernewshackernews7/12/2026
Related Claims
VerifiedLarge language models are essentially probability prediction systems based on the Transformer architecture, generating text by predicting the next most likely word.82% similarVerifiedCurrent mainstream large language models are essentially probabilistic prediction systems based on the Transformer architecture, generating content by predicting the next most likely token.81% similarUnverifiedLarge language models are probability-based text prediction systems; vague inputs cause the model to sample in a high-entropy state, and outputs tend toward the most common generic answers found in training data78% similarUnverified主流大语言模型基于Transformer架构，通过预训练将知识压缩进数十亿乃至数千亿的模型权重中77% similarVerifiedLarge language models are optimized to make code look correct rather than to meet current-stage requirements; they are fundamentally probability-statistical machines that predict the next most likely token and excel at reproducing high-frequency patterns from training data.76% similar
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