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arXiv · 2408.01969

Optimal and efficient text counterfactuals using Graph Neural Networks

Dimitris Lymperopoulos·Maria Lymperaiou·Giorgos Filandrianos·Giorgos Stamou
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Abstract

As NLP models become increasingly integral to decision-making processes, the need for explainability and interpretability has become paramount. In this work, we propose a framework that achieves the aforementioned by generating semantically edited inputs, known as counterfactual interventions, which change the model prediction, thus providing a form of counterfactual explanations for the model. We test our framework on two NLP tasks - binary sentiment classification and topic classification - and show that the generated edits are contrastive, fluent and minimal, while the whole process remains significantly faster that other state-of-the-art counterfactual editors.

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Dimitris Lymperopoulos, Maria Lymperaiou, Giorgos Filandrianos, Giorgos Stamou. 2024-08-04. Optimal and efficient text counterfactuals using Graph Neural Networks. https://doi.org/10.18653/v1/2024.blackboxnlp-1.1

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