@inproceedings{zhang-etal-2026-queueedit,
title = "{Q}ueue{EDIT}: Structural Self-Correction for Sequential Model Editing in {LLM}s",
author = "Zhang, Taolin and
Kang, Haidong and
Li, Dongyang and
Chen, Qizhou and
He, Xiaofeng and
Wang, Chengyu and
Hong, Richang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.231/",
pages = "4706--4719",
ISBN = "979-8-89176-395-1",
abstract = "Recently, large language models (LLMs) have demonstrated impressive performance but still suffer from hallucinations. Model editing has been proposed as a means to correct factual inaccuracies. A challenging scenario is sequential model editing (SME), which aims to rectify errors continuously, rather than a one-time task. During SME, the general capabilities of LLMs can be negatively affected due to the introduction of new parameters. In this paper, we propose a queue-based self-correction framework, QueueEDIT, that not only enhances SME performance by addressing long-sequence dependencies but also mitigates the impact of parameter bias on the general capabilities of LLMs. Specifically, we first introduce a structural mapping editing loss to map editing triplets to knowledge-sensitive neurons within the Transformer layers. We then store the located parameters for each piece of edited knowledge in a queue and dynamically align previously edited parameters. At each edit, we select parameters in the queue that are most relevant to currently located parameters to determine whether knowledge associated with previous edits requires realignment. Irrelevant parameters in the queue are frozen, and we update the parameters at the queue head into the LLM to ensure they do not harm general capabilities. Experiments show that QueueEDIT significantly outperforms strong baselines across various SME settings, while maintaining competitive performance in single-turn editing. Resulting LLMs also preserve high performance on general NLP tasks throughout the SME process."
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<abstract>Recently, large language models (LLMs) have demonstrated impressive performance but still suffer from hallucinations. Model editing has been proposed as a means to correct factual inaccuracies. A challenging scenario is sequential model editing (SME), which aims to rectify errors continuously, rather than a one-time task. During SME, the general capabilities of LLMs can be negatively affected due to the introduction of new parameters. In this paper, we propose a queue-based self-correction framework, QueueEDIT, that not only enhances SME performance by addressing long-sequence dependencies but also mitigates the impact of parameter bias on the general capabilities of LLMs. Specifically, we first introduce a structural mapping editing loss to map editing triplets to knowledge-sensitive neurons within the Transformer layers. We then store the located parameters for each piece of edited knowledge in a queue and dynamically align previously edited parameters. At each edit, we select parameters in the queue that are most relevant to currently located parameters to determine whether knowledge associated with previous edits requires realignment. Irrelevant parameters in the queue are frozen, and we update the parameters at the queue head into the LLM to ensure they do not harm general capabilities. Experiments show that QueueEDIT significantly outperforms strong baselines across various SME settings, while maintaining competitive performance in single-turn editing. Resulting LLMs also preserve high performance on general NLP tasks throughout the SME process.</abstract>
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%0 Conference Proceedings
%T QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMs
%A Zhang, Taolin
%A Kang, Haidong
%A Li, Dongyang
%A Chen, Qizhou
%A He, Xiaofeng
%A Wang, Chengyu
%A Hong, Richang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhang-etal-2026-queueedit
%X Recently, large language models (LLMs) have demonstrated impressive performance but still suffer from hallucinations. Model editing has been proposed as a means to correct factual inaccuracies. A challenging scenario is sequential model editing (SME), which aims to rectify errors continuously, rather than a one-time task. During SME, the general capabilities of LLMs can be negatively affected due to the introduction of new parameters. In this paper, we propose a queue-based self-correction framework, QueueEDIT, that not only enhances SME performance by addressing long-sequence dependencies but also mitigates the impact of parameter bias on the general capabilities of LLMs. Specifically, we first introduce a structural mapping editing loss to map editing triplets to knowledge-sensitive neurons within the Transformer layers. We then store the located parameters for each piece of edited knowledge in a queue and dynamically align previously edited parameters. At each edit, we select parameters in the queue that are most relevant to currently located parameters to determine whether knowledge associated with previous edits requires realignment. Irrelevant parameters in the queue are frozen, and we update the parameters at the queue head into the LLM to ensure they do not harm general capabilities. Experiments show that QueueEDIT significantly outperforms strong baselines across various SME settings, while maintaining competitive performance in single-turn editing. Resulting LLMs also preserve high performance on general NLP tasks throughout the SME process.
%U https://aclanthology.org/2026.findings-acl.231/
%P 4706-4719
Markdown (Informal)
[QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMs](https://aclanthology.org/2026.findings-acl.231/) (Zhang et al., Findings 2026)
ACL
- Taolin Zhang, Haidong Kang, Dongyang Li, Qizhou Chen, Xiaofeng He, Chengyu Wang, and Richang Hong. 2026. QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 4706–4719, San Diego, California, United States. Association for Computational Linguistics.