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There has been a growing interest in interpreting the underlying dynamics ofTransformers. While self-attention patterns were initially deemed as theprimary option, recent studies have shown that integrating other components canyield more accurate explanations. This paper introduces a novel tokenattribution analysis method that incorporates all the components in the encoderblock and aggregates this throughout layers. Through extensive quantitative andqualitative experiments, we demonstrate that our method can produce faithfuland meaningful global token attributions. Our experiments reveal thatincorporating almost every encoder component results in increasingly moreaccurate analysis in both local (single layer) and global (the whole model)settings. Our global attribution analysis significantly outperforms previousmethods on various tasks regarding correlation with gradient-based saliencyscores. Our code is freely available athttps://github.com/mohsenfayyaz/GlobEnc.

GlobEnc: Quantifying Global Token Attribution by Incorporating the Whole Encoder Layer in Transformers