@inproceedings{you-etal-2025-lrp,
title = "When {LRP} Diverges from Leave-One-Out in Transformers",
author = "You, Weiqiu and
Zeng, Siqi and
Tsai, Yao-Hung Hubert and
Yamada, Makoto and
Zhao, Han",
editor = "Belinkov, Yonatan and
Mueller, Aaron and
Kim, Najoung and
Mohebbi, Hosein and
Chen, Hanjie and
Arad, Dana and
Sarti, Gabriele",
booktitle = "Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.blackboxnlp-1.10/",
pages = "176--188",
ISBN = "979-8-89176-346-3",
abstract = "Leave-One-Out (LOO) provides an intuitive measure of feature importance but is computationally prohibitive. While Layer-Wise Relevance Propagation (LRP) offers a potentially efficient alternative, its axiomatic soundness in modern Transformers remains under-examined. In this work, we first show that the bilinear propagation rules used in recent advances of AttnLRP violate implementation invariance. We prove this analytically and confirm it empirically in linear attention layers. Second, we also revisit CP-LRP as a diagnostic baseline and find that bypassing relevance propagation through the softmax layer{---}back-propagating relevance only through the value matrices{---}significantly improves alignment with LOO, particularly in the middle-to-late Transformer layers. Overall, our results suggest that (i) bilinear factorization sensitivity and (ii) softmax propagation error potentially jointly undermine LRP{'}s ability to approximate LOO in Transformers."
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%0 Conference Proceedings
%T When LRP Diverges from Leave-One-Out in Transformers
%A You, Weiqiu
%A Zeng, Siqi
%A Tsai, Yao-Hung Hubert
%A Yamada, Makoto
%A Zhao, Han
%Y Belinkov, Yonatan
%Y Mueller, Aaron
%Y Kim, Najoung
%Y Mohebbi, Hosein
%Y Chen, Hanjie
%Y Arad, Dana
%Y Sarti, Gabriele
%S Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-346-3
%F you-etal-2025-lrp
%X Leave-One-Out (LOO) provides an intuitive measure of feature importance but is computationally prohibitive. While Layer-Wise Relevance Propagation (LRP) offers a potentially efficient alternative, its axiomatic soundness in modern Transformers remains under-examined. In this work, we first show that the bilinear propagation rules used in recent advances of AttnLRP violate implementation invariance. We prove this analytically and confirm it empirically in linear attention layers. Second, we also revisit CP-LRP as a diagnostic baseline and find that bypassing relevance propagation through the softmax layer—back-propagating relevance only through the value matrices—significantly improves alignment with LOO, particularly in the middle-to-late Transformer layers. Overall, our results suggest that (i) bilinear factorization sensitivity and (ii) softmax propagation error potentially jointly undermine LRP’s ability to approximate LOO in Transformers.
%U https://aclanthology.org/2025.blackboxnlp-1.10/
%P 176-188
Markdown (Informal)
[When LRP Diverges from Leave-One-Out in Transformers](https://aclanthology.org/2025.blackboxnlp-1.10/) (You et al., BlackboxNLP 2025)
ACL
- Weiqiu You, Siqi Zeng, Yao-Hung Hubert Tsai, Makoto Yamada, and Han Zhao. 2025. When LRP Diverges from Leave-One-Out in Transformers. In Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pages 176–188, Suzhou, China. Association for Computational Linguistics.