Unverified60% confidenceFactExact time
Autoregressive generative models sample the next token at each step based on the probability distribution of the preceding context; when a repetitive pattern gains a slightly higher probability, it forms a positive feedback loop that leads to repetition degeneration.
2
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60%
Confidence
Long-term
Relevance
8/15/2026
First Seen
Sources
Related Claims
UnverifiedIn a single one-step denoising step, the model directly reads out the probability distribution for each answer slot rather than sampling token by token.78% similarUnverifiedLarge language models use an autoregressive generation mechanism, sampling the next token at each step based solely on a probability distribution, with each token's generation conditioned on all preceding tokens to form a chain of probabilistic dependencies.76% similarUnverified重复运行以计算通过率比依赖单次结果更能反映模型的真实表现75% similarUnverified自回归模型将联合概率拆解为逐步条件概率的乘积，通过因果注意力掩码确保每个token只能看到左侧历史信息75% similarUnverifiedWord-by-word generation is technically called autoregressive generation, where the token generated at each step is appended back to the input sequence as the condition for the next prediction74% similar
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