Unverified50% confidenceOpinionExact time
Generative models cannot produce calibrated class probabilities, whereas decision models can output a calibrated probability distribution for threshold-based judgment.
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50%
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Long-term
Relevance
10/19/2026
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VerifiedThe probability figures provided by guided generative models are essentially 'well-constrained inferences' rather than directly verifiable truths.79% similarUnverifiedModel capability cannot be judged by parameter scale alone, as model performance is influenced by multiple factors including training data distribution, reasoning pathways, and randomness.75% similarUnverifiedTyped questions pre-declare the value space of the answer, letting the model output only a normalized probability distribution, thereby eliminating parsing errors, format drift, and hallucinations.74% similarUnverified生成式模型建模联合概率 P(x,y) 或数据分布 P(x)，而判别式模型直接建模条件概率 P(y|x)；生成模型在小样本时更具优势，判别模型在大数据量时通常表现更好73% similarUnverifiedNon-determinism stems from the random sampling mechanism of model inference itself as well as vendors' continuous iteration on model versions.72% similar
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