360 Degrees rumor detection: When explanations got some explaining to do

Janssens, Bram; Schetgen, Lisa; Bogaert, Marcus; Meire, Matthijs; Poel, Dirk Van den

doi:10.1016/j.ejor.2023.06.024

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Perturbation-based Methods1

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2024

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Cited by 6 publications

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“…Rodrigues et al extended LIME to meta-embedding input in a model for a semantical textual similarity task with a meta-embedding approach [126]. Janssens et al employed LIME when comparing different models to detect rumors in tweets [127]. Chen et al compared different models to perform Patient Safety Events classification using a proprietary dataset [128].…”

Section: Perturbation-based Methodsmentioning

confidence: 99%

The Explainability of Transformers: Current Status and Directions

Fantozzi,

Naldi

2024

Computers

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An increasing demand for model explainability has accompanied the widespread adoption of transformers in various fields of applications. In this paper, we conduct a survey of the existing literature on the explainability of transformers. We provide a taxonomy of methods based on the combination of transformer components that are leveraged to arrive at the explanation. For each method, we describe its mechanism and survey its applications. We find out that attention-based methods, both alone and in conjunction with activation-based and gradient-based methods, are the most employed ones. A growing attention is also devoted to the deployment of visualization techniques to help the explanation process.

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Section: Perturbation-based Methodsmentioning

confidence: 99%