@inproceedings{cao-etal-2026-r2edit,
title = "{R}{\texttwosuperior}{E}dit: Mastering Relation Replacement Editing via Target-Smoothed Forgetting and Self-Paced Learning",
author = "Cao, Xu and
Chen, Xinrui and
Liu, Qing and
Li, Mengyang and
Wu, Ou and
Du, Yi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.869/",
pages = "20096--20123",
ISBN = "979-8-89176-459-0",
abstract = "Knowledge editing updates facts in large language models while preserving unrelated knowledge. Most prior work studies object editing, which modifies the object under a fixed subject{--}relation pair. We instead study relation replacement editing, where an obsolete relation must be suppressed as a new one is introduced. We systematically study this setting with a dedicated benchmark and method. We introduce $\textbf{ReEditBench}$ to evaluate replacement rather than association addition. Existing editors exhibit two major failures: obsolete-relation retention remains as high as 98.20{\%} after successful edits, and difficult samples often destabilize editing. To address this, we propose $\textbf{R$^2$Edit}$, a two-stage framework for relation replacement. Its target-smoothed Forget stage reduces obsolete-relation activation without fixed/random pseudo-target bias, while its curriculum-guided Edit stage progressively injects new relations from easy to hard samples. Across multiple LLMs, $\textbf{R$^2$Edit}$ improves replacement success, reduces retention, and preserves generalization. Stress tests on difficult subsets further demonstrate the robustness of the Edit stage. Code is available at: \url{https://github.com/caoxu0109/R2Edit}."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="cao-etal-2026-r2edit">
<titleInfo>
<title>R²Edit: Mastering Relation Replacement Editing via Target-Smoothed Forgetting and Self-Paced Learning</title>
</titleInfo>
<name type="personal">
<namePart type="given">Xu</namePart>
<namePart type="family">Cao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xinrui</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Qing</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mengyang</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ou</namePart>
<namePart type="family">Wu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yi</namePart>
<namePart type="family">Du</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hung-Yi</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunipa</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Desmond</namePart>
<namePart type="family">Elliott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-459-0</identifier>
</relatedItem>
<abstract>Knowledge editing updates facts in large language models while preserving unrelated knowledge. Most prior work studies object editing, which modifies the object under a fixed subject–relation pair. We instead study relation replacement editing, where an obsolete relation must be suppressed as a new one is introduced. We systematically study this setting with a dedicated benchmark and method. We introduce ReEditBench to evaluate replacement rather than association addition. Existing editors exhibit two major failures: obsolete-relation retention remains as high as 98.20% after successful edits, and difficult samples often destabilize editing. To address this, we propose R²Edit, a two-stage framework for relation replacement. Its target-smoothed Forget stage reduces obsolete-relation activation without fixed/random pseudo-target bias, while its curriculum-guided Edit stage progressively injects new relations from easy to hard samples. Across multiple LLMs, R²Edit improves replacement success, reduces retention, and preserves generalization. Stress tests on difficult subsets further demonstrate the robustness of the Edit stage. Code is available at: https://github.com/caoxu0109/R2Edit.</abstract>
<identifier type="citekey">cao-etal-2026-r2edit</identifier>
<location>
<url>https://aclanthology.org/2026.emnlp-main.869/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>20096</start>
<end>20123</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T R²Edit: Mastering Relation Replacement Editing via Target-Smoothed Forgetting and Self-Paced Learning
%A Cao, Xu
%A Chen, Xinrui
%A Liu, Qing
%A Li, Mengyang
%A Wu, Ou
%A Du, Yi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F cao-etal-2026-r2edit
%X Knowledge editing updates facts in large language models while preserving unrelated knowledge. Most prior work studies object editing, which modifies the object under a fixed subject–relation pair. We instead study relation replacement editing, where an obsolete relation must be suppressed as a new one is introduced. We systematically study this setting with a dedicated benchmark and method. We introduce ReEditBench to evaluate replacement rather than association addition. Existing editors exhibit two major failures: obsolete-relation retention remains as high as 98.20% after successful edits, and difficult samples often destabilize editing. To address this, we propose R²Edit, a two-stage framework for relation replacement. Its target-smoothed Forget stage reduces obsolete-relation activation without fixed/random pseudo-target bias, while its curriculum-guided Edit stage progressively injects new relations from easy to hard samples. Across multiple LLMs, R²Edit improves replacement success, reduces retention, and preserves generalization. Stress tests on difficult subsets further demonstrate the robustness of the Edit stage. Code is available at: https://github.com/caoxu0109/R2Edit.
%U https://aclanthology.org/2026.emnlp-main.869/
%P 20096-20123
Markdown (Informal)
[R²Edit: Mastering Relation Replacement Editing via Target-Smoothed Forgetting and Self-Paced Learning](https://aclanthology.org/2026.emnlp-main.869/) (Cao et al., EMNLP 2026)
ACL