Interactively Generating Explanations for Transformer Language Models.
Year: 2021
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Patrick Schramowski, Felix Friedrich, Christopher Tauchmann, Kristian Kersting
Institution: Technische Universität Darmstadt
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3202537109