Papers by Lucas Resck
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. |
Explainability and Interpretability of Multilingual Large Language Models: A Survey (2025.emnlp-main)
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| Challenge: | Existing literature on multilingual large language models lacks transparency in their internal processes. |
| Approach: | They propose to use multilingual large language models to examine their explainability and interpretability methods. |
| Outcome: | The present study examines the explainability and interpretability of multilingual large language models. |