Interactively Providing Explanations for Transformer Language Models

TL;DR

This paper introduces an interactive approach to explain transformer language models by integrating prototype networks into their architecture, allowing for better interpretability and human-in-the-loop learning.

Contribution

It proposes a novel architecture that combines prototype networks with transformers, enabling direct explanations and interactive learning from users.

Findings

Performs on par with existing language models.

Enables learning from user interactions.

Improves interpretability of transformer models.

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. Our architecture performs on par with several language models and, moreover, enables learning 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.