| Challenge: | Input saliency methods have become popular for explaining predictions of deep learning models, but there has been little work investigating methods for aggregating prediction-level explanations to the class level. |
| Approach: | They propose a method to aggregate prediction-level explanations to the class level using XLM-R and Integrated Gradients input attribution methods. |
| Outcome: | The proposed method extracts keyword lists of classes from text classification tasks and evaluates them on web register data. |
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| Challenge: | Explainable Artificial Intelligence (XAI) is aimed at providing explanations for decisions made by AI systems. |
| Approach: | They propose to use model-agnostic and model-specific explanation methods for CNNs for text classification to provide human-grounded evaluations. |
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Discretized Integrated Gradients for Explaining Language Models (2021.emnlp-main)
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| Challenge: | Integrated Gradients (IG) is widely adopted due to its desirable explanation axioms and the ease of gradient computation. |
| Approach: | They propose an attribution-based explanation algorithm that uses averaging the model's output gradient interpolated along a straight-line path in the input data space. |
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A Hierarchical Explanation Generation Method Based on Feature Interaction Detection (2023.findings-acl)
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| Challenge: | Existing work on hierarchical attributions tends to limit text groups to a continuous text span, which is difficult for humans to read. |
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Multi-Level Explanations for Generative Language Models (2025.acl-long)
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Lucas Monteiro Paes, Dennis Wei, Hyo Jin Do, Hendrik Strobelt, Ronny Luss, Amit Dhurandhar, Manish Nagireddy, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Werner Geyer, Soumya Ghosh
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Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection (2020.acl-main)
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| Challenge: | Existing methods for generating explanations for neural networks ignore feature interactions between words and phrases. |
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Interpreting Predictions of NLP Models (2020.emnlp-tutorials)
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| Challenge: | This tutorial will provide a background on interpretation techniques for neural NLP models. |
| Approach: | This tutorial will provide a background on interpretation techniques for NLP models . it will examine saliency maps, input perturbations, adversarial attacks and influence functions . |
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SHAP-Based Explanation Methods: A Review for NLP Interpretability (2022.coling-1)
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| Challenge: | Existing models with opacity problems have been proposed to address this problem. |
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Saliency Learning: Teaching the Model Where to Pay Attention (N19-1)
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| Challenge: | Recent work on explanation and interpretation has introduced methods to provide insights toward the model’s behaviour and predictions, but they do not improve the model's reliability. |
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Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students? (2022.tacl-1)
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Danish Pruthi, Rachit Bansal, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C. Lipton, Graham Neubig, William W. Cohen
| Challenge: | Existing methods to explain predictions by highlighting salient features are often unstated. |
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Latent Concept-based Explanation of NLP Models (2024.emnlp-main)
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| Challenge: | Existing attempts to explain deep learning models rely on input features, such as the words . however, such explanations are often less informative due to the discrete nature of words and lack of contextual verbosity. |
| Approach: | They propose a method that generates explanations for predictions based on latent concepts . they map the representations of salient input words into the training latent space . |
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