Papers by Marcos M. Raimundo
Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales (2024.findings-naacl)
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| Challenge: | Saliency post-hoc explainability methods are important tools for understanding complex NLP models, but they may not align with human intuition, making the explanations not plausible. |
| Approach: | They propose a method for incorporating rationales into text classification models by augmenting the standard cross-entropy loss with a novel loss function inspired by contrastive learning. |
| Outcome: | The proposed approach enhances the plausibility of post-hoc explanations while preserving their faithfulness. |