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Pretrained transformers achieve the state of the art across tasks in naturallanguage processing, motivating researchers to investigate their innermechanisms. One common direction is to understand what features are importantfor prediction. In this paper, we apply information bottlenecks to analyze theattribution of each feature for prediction on a black-box model. We use BERT asthe example and evaluate our approach both quantitatively and qualitatively. Weshow the effectiveness of our method in terms of attribution and the ability toprovide insight into how information flows through layers. We demonstrate thatour technique outperforms two competitive methods in degradation tests on fourdatasets. Code is available at https://github.com/bazingagin/IBA.

Inserting Information Bottlenecks for Attribution in Transformers