Papers with Feature attribution methods
CafGa: Customizing Feature Attributions to Explain Language Models (2025.emnlp-demos)
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| Challenge: | Feature attribution methods, such as SHAP and LIME, quantify the influence of each input component in a model. |
| Approach: | They propose a tool for generating and evaluating feature attribution explanations at customizable granularities. |
| Outcome: | The proposed tool is compared with two baseline methods: PartitionSHAP and MExGen. |
Normalized AOPC: Fixing Misleading Faithfulness Metrics for Feature Attributions Explainability (2025.acl-long)
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Joakim Edin, Andreas Geert Motzfeldt, Casper L. Christensen, Tuukka Ruotsalo, Lars Maaløe, Maria Maistro
| Challenge: | Deep neural network predictions are notoriously difficult to interpret due to the difficulty in understanding their inner mechanisms. |
| Approach: | They propose to normalize AOPC to enable consistent cross-model evaluations and more meaningful interpretation of individual scores. |
| Outcome: | The proposed approach can radically change AOPC results, questioning the conclusions of earlier studies and offering a more robust framework for assessing feature attribution faithfulness. |
The Solvability of Interpretability Evaluation Metrics (2023.findings-eacl)
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| Challenge: | Feature attribution methods are often evaluated on metrics such as comprehensiveness and sufficiency. |
| Approach: | They propose to use beam search to define problem of optimizing an explanation for a metric . they also propose to evaluate the metric on one or more metrics to determine its solvability . |
| Outcome: | The proposed explainer can solve the problem of optimizing an explanation for a metric by beam search. |
Incorporating Attribution Importance for Improving Faithfulness Metrics (2023.acl-long)
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| Challenge: | Feature attribution methods (FAs) are popular for providing insights into the model reasoning process of making predictions. |
| Approach: | They propose a simple yet effective criterion that randomly masks tokens proportionately to their FA importance. |
| Outcome: | The proposed method is more faithful than hard sufficiency and comprehensiveness metrics. |
Incorporating Priors with Feature Attribution on Text Classification (P19-1)
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| Challenge: | Feature attribution methods are used to help users interpret complex models. |
| Approach: | They propose a feature attribution method that integrates feature attributed features into the objective function to allow machine learning practitioners to incorporate priors in model building. |
| Outcome: | The proposed method reduces undesired model biases without a tradeoff on the original task and improves classifier performance in scarce data setting. |