Unverified50% confidenceFactExact time
LIME works by applying small perturbations to the input within the local neighborhood of the sample to be explained, observing changes in model output, and then approximating local behavior with a simple linear model; it is a model-agnostic method that can provide explanations for any black-box model.
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9/25/2026
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VerifiedLIME通过在目标样本周围构造扰动数据集，训练局部可解释的线性模型来近似复杂模型的局部行为77% similarVerifiedLIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) are the primary tools for providing post-hoc explanations for complex black-box models.75% similarVerifiedKnowledge distillation was systematized by Hinton et al. in 2015; its core idea is to transfer the dark knowledge of a large model to a small model, using soft probability distributions as supervision signals.71% similarUnverifiedThe core method of the experiment is to fine-tune a small causal language model on a corpus that lies everywhere, and then observe how its confidence changes.71% similarUnverifiedLIME provides local rather than global explanations, guaranteeing reliability only in the vicinity of the current sample and cannot fully describe the model's decision logic across the entire feature space.70% similar
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