Papers by Loris Schoenegger
Compact Example-Based Explanations for Language Models (2026.findings-acl)
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| Challenge: | Existing training data influence estimation methods rely on naive selection strategies to provide explanations of a human-interpretable size. |
| Approach: | They propose a retraining-free metric that quantifies how useful a set of examples is for explaining a model's output. |
| Outcome: | The proposed model can predict whether a set of examples supports or undermines the model’s predictions. |