Interactively Generating Explanations for Transformer Language Models

• URL: http://arxiv.org/abs/2110.02058v3
• Date: Thu, 7 Oct 2021 08:00:10 GMT
• Title: Interactively Generating Explanations for Transformer Language Models
• Authors: Patrick Schramowski, Felix Friedrich, Christopher Tauchmann, and Kristian Kersting
• Abstract summary: Transformer language models are state-of-the-art in a multitude of NLP tasks. Recent methods aim to provide interpretability and explainability to black-box models. We emphasize using prototype networks directly incorporated into the model architecture.
• Score: 14.306470205426526
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformer language models are state-of-the-art in a multitude of NLP tasks. Despite these successes, their opaqueness remains problematic. Recent methods aiming to provide interpretability and explainability to black-box models primarily focus on post-hoc explanations of (sometimes spurious) input-output correlations. Instead, we emphasize using prototype networks directly incorporated into the model architecture and hence explain the reasoning process behind the network's decisions. Moreover, while our architecture performs on par with several language models, it enables one to learn from user interactions. This not only offers a better understanding of language models but uses human capabilities to incorporate knowledge outside of the rigid range of purely data-driven approaches.

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