Unverified50% confidenceSolutionExact time
In quantization evaluation, having the original model generate the dataset itself and computing KLD on these generated tokens with zero weight assigned to prompt tokens can better reflect real inference scenarios.
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10/24/2026
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UnverifiedUpon each new observation, SLICE generates simulated data from the corresponding model for each candidate null hypothesis parameter value, and uses it to conformalize positive-predictable scores, converting them into conditional e-values.75% similarUnverified大模型推理的自回归生成需要逐Token串行输出，生成N个Token需要N次前向传播73% similarVerifiedSpeculative decoding is a lossless acceleration technique that does not alter the model's output distribution; it uses a draft model with far fewer parameters than the target model to generate candidate tokens, which the target model then verifies in parallel.72% similarVerifiedSpeculative decoding generates candidate tokens through a lightweight draft model, with the main model verifying them in parallel in a single forward pass, accepting tokens that conform to the distribution and regenerating from the first rejected position.72% similarUnverifiedJudging whether a quantized model is suitable for a specific task ultimately still requires evaluation in real scenarios and cannot rely on KLD alone.71% similar
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