Papers with sequential editing
Model Merging for Knowledge Editing (2025.acl-industry)
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Zichuan Fu, Xian Wu, Guojing Li, Yingying Zhang, Yefeng Zheng, Tianshi Ming, Yejing Wang, Wanyu Wang, Xiangyu Zhao
| Challenge: | Existing knowledge editing approaches struggle with sequential editing scenarios and harm the general capabilities of the model. |
| Approach: | They propose a framework that combines robust supervised fine-tuning and model merging for knowledge editing to combine supervised and supervised learning. |
| Outcome: | The proposed approach outperforms existing methods in sequential editing while preserving the original performance of the model. |
DAFNet: Dynamic Auxiliary Fusion for Sequential Model Editing in Large Language Models (2024.findings-acl)
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Taolin Zhang, Qizhou Chen, Dongyang Li, Chengyu Wang, Xiaofeng He, Longtao Huang, Hui Xue’, Jun Huang
| Challenge: | Large language models (LLMs) have shown impressive results, but still suffer from hallucination, i.e., the generation of false information. |
| Approach: | They propose a task of sequential model editing that aims to rectify mistakes continuously. |
| Outcome: | The proposed method significantly outperforms baselines in single-turn and sequential editing. |
Learning to Edit: Aligning LLMs with Knowledge Editing (2024.acl-long)
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Yuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu, Wei Wang
| Challenge: | Existing knowledge editing techniques rely on memorizing updated knowledge, impeding LLMs from effectively combining the new knowledge with their inherent knowledge when answering questions. |
| Approach: | They propose a Learning to Edit framework that equips LLMs with the ability to apply updated knowledge to input questions through a two-phase process . |
| Outcome: | The proposed framework outperforms existing methods in knowledge editing tasks and compares it with four benchmarks and two LLM architectures. |
Constraining Sequential Model Editing with Editing Anchor Compression (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) exhibit hallucinations due to incorrect or outdated knowledge embedded in their parameters. |
| Approach: | They propose a framework to constrain the deviation of the parameter matrix during sequential editing by selecting editing anchors that are important in encoding new relations without deviating too much from the original matrix. |
| Outcome: | The proposed framework minimizes deviations caused by model editing while retaining over 70% of the general abilities. |
The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse (2024.findings-acl)
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| Challenge: | Even a single edit can trigger model collapse, manifesting as significant performance degradation in various benchmark tasks. |
| Approach: | They propose to use perplexity as a surrogate metric to determine whether an edited model's performance is affected by a single edit. |
| Outcome: | The proposed method shows that even a single edit can cause model collapse, manifesting as significant performance degradation in various benchmark tasks. |
Can We Continually Edit Language Models? On the Knowledge Attenuation in Sequential Model Editing (2024.findings-acl)
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| Challenge: | Existing methods for model editing suffer from knowledge attenuation due to redundant parameters interference and update weight disentanglement. |
| Approach: | They propose a method to mitigate the problem of knowledge attenuation in sequential editing by analyzing redundant parameters interference and update weight disentanglement. |
| Outcome: | The proposed method mitigates the knowledge attenuation issue and improves on existing benchmarks. |
LyapLock: Bounded Knowledge Preservation in Sequential Large Language Model Editing (2025.emnlp-main)
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| Challenge: | Existing models for enhancing knowledge updating are prone to performance degradation due to incomplete knowledge preservation mechanisms. |
| Approach: | They propose a model for locate-then-edit that decomposes long-term constrained programming into tractable stepwise subproblems for efficient solving. |
| Outcome: | The proposed framework achieves asymptotic optimal editing performance while meeting the constraints of long-term knowledge preservation. |
KoLEG: On-the-Fly Korean Legal Knowledge Editing with Continuous Retrieval (2025.findings-emnlp)
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Jaehyung Seo, Dahyun Jung, Jaewook Lee, Yongchan Chun, Dongjun Kim, Hwijung Ryu, Donghoon Shin, Heuiseok Lim
| Challenge: | a recent study shows that Korean legal knowledge is subject to frequent temporal updates driven by societal needs and government policies. |
| Approach: | They propose a Korean Legal knowledge editing framework enhanced with continuous retrieval . they employ an Editing-Aware Learning Strategy and a LawEdit Retriever . |
| Outcome: | a new framework outperforms existing methods for updating legal knowledge in Korean . it maintains robust performance in sequential editing and is qualitatively validated by legal experts. |
Navigating the Dual Facets: A Comprehensive Evaluation of Sequential Memory Editing in Large Language Models (2024.acl-long)
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Zihao Lin, Mohammad Beigi, Hongxuan Li, Yufan Zhou, Yuxiang Zhang, Qifan Wang, Wenpeng Yin, Lifu Huang
| Challenge: | Memory Editing (ME) has emerged as an efficient method to modify erroneous facts or inject new knowledge into Large Language Models (LLMs). |
| Approach: | They propose to evaluate LLMs with single edit only and parameter-modifying ME with parameter-preserving ME. |
| Outcome: | The proposed method can maintain LLMs’ fundamental capabilities but struggles to accurately recall edited knowledge presented in a different format. |
The Mirage of Model Editing: Revisiting Evaluation in the Wild (2025.acl-long)
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| Challenge: | despite near-perfect results, effectiveness of model editing in real-world applications remains unclear. |
| Approach: | They propose QAEdit and WILD to better reflect real-world use of model editing . they propose a benchmark aligned with widely used question answering datasets and a task-agnostic evaluation framework . |
| Outcome: | The proposed QAEdit benchmark and WILD evaluation framework show that current models perform worse than previously reported. |
Rebuilding ROME : Resolving Model Collapse during Sequential Model Editing (2024.emnlp-main)
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| Challenge: | Recent work using Rank-One Model Editing (ROME) has shown that there are certain facts that the algorithm is unable to edit without breaking the model. |
| Approach: | They propose to use a model editing method called Rank-One Model Editing to make multiple edits to a single model without breaking it. |
| Outcome: | The proposed method improves generalization and locality of model editing and improves model collapse compared to the original implementation of ROME. |
Lifelong Knowledge Editing requires Better Regularization (2025.findings-emnlp)
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Akshat Gupta, Phudish Prateepamornkul, Maochuan Lu, Ahmed Alaa, Thomas Hartvigsen, Gopala Anumanchipalli
| Challenge: | Knowledge editing is a promising way to improve factuality in large language models, but recent studies have shown significant model degradation during sequential editing. |
| Approach: | They formalize locate-then-edit methods as a two-step fine-tuning process . they show that model degradation occurs due to over-optimization of internal activations . |
| Outcome: | The proposed methods reduce time and improve factuality by 42-61%. |