@inproceedings{azarkhalili-etal-2026-pr,
    title = "{PR}-{XAI}: {P}age{R}ank-Based Feature Attribution for Transformers",
    author = "Azarkhalili, Behrooz  and
      Li, Linyi  and
      Libbrecht, Maxwell W.",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.22/",
    pages = "534--554",
    ISBN = "979-8-89176-390-6",
    abstract = "We introduce PR-XAI, a feature attribution method for transformer models based on the PageRank algorithm. The proposed PR-XAI models the attention mechanism as a directed graph, with weights derived from attention weights and their gradients. Evaluations across five well-known text classification datasets and three different architectures show that PR-AG, one variant of PR-XAI, outperforms state-of-the-art attribution methods in faithfulness and classification metrics, with significant gains on long-form text."
}