Quantifying Context Mixing in Transformers

TL;DR

This paper introduces Value Zeroing, a new method for analyzing how Transformers mix information across entire encoder blocks, providing deeper insights than traditional attention-based analyses.

Contribution

It proposes Value Zeroing, a novel context mixing score that considers the whole encoder, improving understanding of information flow in Transformer models.

Findings

Value Zeroing outperforms existing analysis methods.

It offers more faithful insights into model decision processes.

Demonstrates effectiveness through multiple evaluation approaches.

Abstract

Self-attention weights and their transformed variants have been the main source of information for analyzing token-to-token interactions in Transformer-based models. But despite their ease of interpretation, these weights are not faithful to the models' decisions as they are only one part of an encoder, and other components in the encoder layer can have considerable impact on information mixing in the output representations. In this work, by expanding the scope of analysis to the whole encoder block, we propose Value Zeroing, a novel context mixing score customized for Transformers that provides us with a deeper understanding of how information is mixed at each encoder layer. We demonstrate the superiority of our context mixing score over other analysis methods through a series of complementary evaluations with different viewpoints based on linguistically informed rationales, probing, and faithfulness analysis.