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In explainable artificial intelligence, attention is not explanation refers to the thesis that attention weights often do not correlate with feature importance and so cannot be read as explanations of predictions. This meaning comes from Jain (2019) in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers).[1]
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