Unverified60% confidenceFactExact time
Mainstream LLMs such as the GPT series use a decoder-only autoregressive architecture that generates text by predicting the probability distribution of the next token, with a training objective of minimizing cross-entropy loss.
2
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60%
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
VerifiedThe GPT series uses the Decoder-only variant of the Transformer, predicting the next Token autoregressively.76% similarUnverifiedGPT-series models are optimized during training to maximize prediction accuracy (i.e., minimize perplexity), so their outputs tend toward high-probability word combinations.75% similarVerifiedBERT's bidirectional encoding capability stems from the masked language model (MLM) objective used during its pre-training phase, which contrasts with the unidirectional autoregressive pre-training of the GPT series.74% similarUnverifiedGPT 系列、Llama 等模型的核心能力绝大部分来自预训练而非后续微调阶段73% similarUnverifiedGPT-4o-mini compresses model size through knowledge distillation, using the outputs of a large model as soft labels to train a smaller model.73% similar
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