Papers with Generalized Attention Flow
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. |