@inproceedings{takahashi-etal-2025-understanding,
title = "Understanding the Side Effects of Rank-One Knowledge Editing",
author = "Takahashi, Ryosuke and
Kamoda, Go and
Heinzerling, Benjamin and
Sakaguchi, Keisuke and
Inui, Kentaro",
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-1.11/",
pages = "189--205",
ISBN = "979-8-89176-346-3",
abstract = "This study conducts a detailed analysis of the side effects of rank-one knowledge editing using language models with controlled knowledge. The analysis focuses on each element of knowledge triples (subject, relation, object) and examines two aspects: ``knowledge that causes large side effects when edited'' and ``knowledge that is affected by the side effects.'' Our findings suggest that editing knowledge with subjects that have relationships with numerous objects or are robustly embedded within the LM may trigger extensive side effects. Furthermore, we demonstrate that the similarity between relation vectors, the density of object vectors, and the distortion of knowledge representations are closely related to how susceptible knowledge is to editing influences. The findings of this research provide new insights into the mechanisms of side effects in LM knowledge editing and indicate specific directions for developing more effective and reliable knowledge editing methods."
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%0 Conference Proceedings
%T Understanding the Side Effects of Rank-One Knowledge Editing
%A Takahashi, Ryosuke
%A Kamoda, Go
%A Heinzerling, Benjamin
%A Sakaguchi, Keisuke
%A Inui, Kentaro
%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 takahashi-etal-2025-understanding
%X This study conducts a detailed analysis of the side effects of rank-one knowledge editing using language models with controlled knowledge. The analysis focuses on each element of knowledge triples (subject, relation, object) and examines two aspects: “knowledge that causes large side effects when edited” and “knowledge that is affected by the side effects.” Our findings suggest that editing knowledge with subjects that have relationships with numerous objects or are robustly embedded within the LM may trigger extensive side effects. Furthermore, we demonstrate that the similarity between relation vectors, the density of object vectors, and the distortion of knowledge representations are closely related to how susceptible knowledge is to editing influences. The findings of this research provide new insights into the mechanisms of side effects in LM knowledge editing and indicate specific directions for developing more effective and reliable knowledge editing methods.
%U https://aclanthology.org/2025.blackboxnlp-1.11/
%P 189-205
Markdown (Informal)
[Understanding the Side Effects of Rank-One Knowledge Editing](https://aclanthology.org/2025.blackboxnlp-1.11/) (Takahashi et al., BlackboxNLP 2025)
ACL
- Ryosuke Takahashi, Go Kamoda, Benjamin Heinzerling, Keisuke Sakaguchi, and Kentaro Inui. 2025. Understanding the Side Effects of Rank-One Knowledge Editing. In Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pages 189–205, Suzhou, China. Association for Computational Linguistics.