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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2408.01969 |
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| _version_ | 1866913968786767872 |
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| author | Lymperopoulos, Dimitris Lymperaiou, Maria Filandrianos, Giorgos Stamou, Giorgos |
| author_facet | Lymperopoulos, Dimitris Lymperaiou, Maria Filandrianos, Giorgos Stamou, Giorgos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01969 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimal and efficient text counterfactuals using Graph Neural Networks Lymperopoulos, Dimitris Lymperaiou, Maria Filandrianos, Giorgos Stamou, Giorgos Computation and Language 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. |
| title | Optimal and efficient text counterfactuals using Graph Neural Networks |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2408.01969 |