Layer-wise relevance propagation is a method used to explain the predictions of deep learning models by attributing the output decision to the input features. It works by propagating relevance scores from the output layer back to the input layer, helping to identify which parts of the input data were most influential in making a prediction. This technique enhances the interpretability of complex models, particularly in natural language processing, where understanding the rationale behind decisions is crucial.
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