Unverified50% confidenceFactExact time
Scoring-based classification has the model compute a logit or similarity score for each candidate answer individually, which is normalized via softmax to obtain a probability distribution; inference typically requires only a single forward computation, without autoregressive token-by-token decoding.
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10/29/2026
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UnverifiedCommon implementations of per-answer probability output include adding a classification head at the output layer, scoring candidate answers by likelihood, or normalizing candidates with a softmax over logits.79% similarUnverified分类模型的推理是非自回归的，仅需一次前向传播即可完成所有位置预测，计算效率显著高于生成模型78% similarUnverifiedFor multiple-choice questions, the logits over each option token can be normalized via softmax to obtain a probability distribution, where a highly concentrated distribution indicates the model is more confident in the answer76% 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.75% similarUnverifiedIn the transductive paradigm, computation is evenly distributed across each output token; in the inductive paradigm, the model can invest large amounts of computation in reasoning tokens while the final answer tokens are relatively lightweight.75% similar
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