Grad-SAM: Explaining Transformers via Gradient Self-Attention Maps
Oren Barkan,
Edan Hauon,
Avi Caciularu
et al.
Abstract:Transformer-based language models significantly advanced the stateof-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 … Show more
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