Papers with NLE models
Faithfulness Tests for Natural Language Explanations (2023.acl-short)
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Pepa Atanasova, Oana-Maria Camburu, Christina Lioma, Thomas Lukasiewicz, Jakob Grue Simonsen, Isabelle Augenstein
| Challenge: | Existing methods for explaining neural models are misleading as they often present reasons that are unfaithful to the model’s inner workings. |
| Approach: | They propose a counterfactual input editor for inserting reasons that lead to counterfacts but are not reflected by the NLEs. |
| Outcome: | The proposed model can evaluate emerging NLE models, proving a fundamental tool in the development of faithful explanations. |
KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations (2023.acl-short)
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| Challenge: | eIA is an adversarial attack that generates inconsistent natural language explanations (NLEs) a model that generate In-NLE is undesirable, as it has a faulty decision-making process or is prone to inconsistencies. |
| Approach: | They propose an off-the-shelf mitigation method to alleviate inconsistencies by grounding the model into external background knowledge. |
| Outcome: | The proposed method reduces inconsistencies detected by previous models . it is based on external knowledge bases and a novel approach to mitigate inconsistent models based upon the proposed method . |