Papers with feature attribution method

3 papers
PR-XAI: PageRank-Based Feature Attribution for Transformers (2026.acl-long)

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Challenge: Existing feature attribution methods for transformer models suffer from limitations that undermine their efficacy.
Approach: They propose a feature attribution method for transformer models based on PageRank . they propose attribution methods that apply PageRank to attention-derived graphs .
Outcome: The proposed method outperforms state-of-the-art methods in faithfulness and classification metrics with significant gains on long-form text.
Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection (2022.naacl-main)

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Challenge: XAI features usually provide a single importance score for each token, but feature attribution methods provide two complementary and theoretically-grounded scores for each utterance.
Approach: They propose a feature attribution method that generates explicit perturbations of the input text, allowing the importance scores themselves to be explainable.
Outcome: The proposed method explain the predictions of hate speech detection models on a set of curated examples from a test suite.
Generalized Attention Flow: Feature Attribution for Transformer Models via Maximum Flow (2025.acl-long)

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Challenge: Existing approaches to feature attributions rely on attention weights and attention weightings.
Approach: They propose a feature attribution method that replaces attention weights with the generalized Information Tensor to enhance the performance of Transformer-based models.
Outcome: The proposed method outperforms state-of-the-art feature attribution methods on sequence classification tasks and provides a more reliable interpretation of Transformer model outputs.

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