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arXiv · 2204.11073

Grad-SAM: Explaining Transformers via Gradient Self-Attention Maps

Oren Barkan·Edan Hauon·Avi Caciularu·Ori Katz·Itzik Malkiel·Omri Armstrong·Noam Koenigstein
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Abstract

Transformer-based language models significantly advanced the state-of-the-art in many linguistic tasks. As this revolution continues, the ability to explain model predictions has become a major area of interest for the NLP community. In this work, we present Gradient Self-Attention Maps (Grad-SAM) - a novel gradient-based method that analyzes self-attention units and identifies the input elements that explain the model's prediction the best. Extensive evaluations on various benchmarks show that Grad-SAM obtains significant improvements over state-of-the-art alternatives.

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Oren Barkan, Edan Hauon, Avi Caciularu, Ori Katz, Itzik Malkiel, Omri Armstrong, Noam Koenigstein. 2022-04-23. Grad-SAM: Explaining Transformers via Gradient Self-Attention Maps. https://doi.org/10.1145/3459637.3482126

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