This paper systematically reviews existing methods for evaluating attribution scores and summarizes the logic traps in these methods.

Sep 12, 2021 · Abstract:Modern deep learning models are notoriously opaque, which has motivated the development of methods for interpreting how deep models ...

Table 3: Evaluation results of three attribution methods. T / 1 refers to higher / lower scores are better. del, rep, and pad refer to different modification ...

Mar 4, 2023 · This paper systematically reviews existing methods for evaluating attribution scores and summa- rizes the logic traps in these methods. We fur-.

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Logic traps in evaluating attribution scores. Y Ju, Y Zhang, Z Yang, Z Jiang ... evaluating the stability of feature attribution explanation methods via ...

Logic Traps in Evaluating Attribution Scores. ACL (1) 2022: 5911-5922. [c3]. view. electronic edition via DOI; unpaywalled version; references & citations.

Logic Traps in Evaluating Attribution Scores. In Proceedings of ACL 2022 (Long Paper), Dublin, Ireland, May 22-27. Guirong Bai, Shizhu He, Kang Liu, Jun ...

“Logic traps in evaluating attribution scores,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Dublin ...

Apr 15, 2024 · Logic traps in evaluating attribution scores. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics ...