Papers by Maxwell W. Libbrecht

2 papers
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.
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.

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