Unverified50% confidenceFactExact time
SHAP values measure the contribution of features to model predictions and are based on statistical correlation rather than causal effects, so they cannot prove that temperature is a direct causal driver of strength.
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50%
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10/19/2026
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UnverifiedSHAP的核心思想是将每个特征对模型预测的贡献量化，使得所有特征贡献之和等于模型预测值与基线值的差71% similarUnverifiedModel capability evaluation must fix inference parameters (temperature, top-p, etc.); otherwise, the same model may exhibit vastly different performance under different parameter configurations.66% similarUnverifiedThe autoregressive token prediction paradigm is built on correlation rather than causality.65% similarUnverifiedSHAP用于特征归因（解释每个特征对预测的贡献度），DiCE用于生成多样化的反事实样本，二者互补64% similarUnverifiedCommon attribution methods include Shapley values, counterfactual reasoning and feature importance ranking, which are often presented in prediction functions as contribution rankings or driver factor lists.64% similar
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