Improving performance of deep learning models with axiomatic attribution priors and expected gradients

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The Unreliability of Explanations in Few-Shot In-Context Learning (2022, background): “…the explanations, which is typically done by interpreting a prediction post-hoc through explanation techniques and optimize the distance between the obtained explanation and ground truth explanation (Liu and Avci, 2019; Rieger et al., 2020; Plumb et al., 2020; Erion et al., 2021; Yao et al., 2021).”
The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning (2022, background): “Another line of work seeks to align the reasoning process of a trained model with the explanations, which is typically done by interpreting a prediction post-hoc through explanation techniques and optimizing the distance between the obtained explanation and ground truth explanation (Liu and Avci, 20”