Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery (2025.findings-acl)
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| Challenge: | Existing concepts-based explainable approaches do not discover unseen concepts . a recent approach to solve this problem is concept-based explanations . |
| Approach: | They propose a framework that extracts comprehensible concepts automatically with no annotations . ECO-Concept uses an object-centric architecture to extract task-specific semantic concepts . |
| Outcome: | a new framework extracts comprehensible concepts with no concept annotations . the proposed framework outperforms existing methods in computability tests on diverse tasks . |
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| Challenge: | Existing methods to extract concepts from pre-trained language models are not suitable for commonsense explanation generation. |
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| Challenge: | Existing methods for explaining "black-box" models such as Influence Functions are becoming more popular. |
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| Challenge: | Understanding unexplored data is a slow process, and there is no labeled data at hand. |
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| Challenge: | Existing models that explain complex decisions are limited because of their lack of interpretability. |
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| Challenge: | Existing explainability techniques that can be produced post-hoc with already trained models are lacking a definitive guide on how to choose one given a particular task and model architecture. |
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