SpecEdit: A Spectral Approach for Multi-Round Knowledge Editing (2026.findings-acl)
Copied to clipboard
| Challenge: | Multi-round knowledge editing suffers from performance degradation as edits accumulate . intrinsic knowledge of model and historical edit memories are naively coupled during editing . SpecEdit improves model editing performance by reducing destructive coupling . |
| Approach: | They propose a spectral-based model editing module that integrates into existing editing methods without altering their original optimization procedures. |
| Outcome: | The proposed model improves performance on multiple LLMs and editing methods. |
Similar Papers
Spectral Characterization and Mitigation of Sequential Knowledge Editing Collapse (2026.acl-long)
Copied to clipboard
Chi Zhang, Mengqi Zhang, Xiaotian Ye, Runxi Cheng, Zisheng Zhou, Ying Zhou, Pengjie Ren, Zhumin Chen
| Challenge: | Existing approaches to reducing the effects of knowledge editing are insufficiently understood. |
| Approach: | They propose a plug-and-play framework that preserves the dominant subspace of the original weights and analyzes parameter updates in the spectral basis of the weights. |
| Outcome: | The proposed framework improves editing efficacy while preserving general abilities under long-horizon sequential editing, including extreme settings with up to 20,000 edits. |
RuleEdit: Towards Rule-Level Knowledge Generalization to Mitigate Over-Editing in Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing knowledge editing methods focus on instance-level editing, which is prone to knowledge degradation and general ability deterioration due to redundant instance-specific modifications. |
| Approach: | They propose a rule-level editing method that generalizes rule-derived knowledge to update rule-based instances. |
| Outcome: | The proposed method improves portability and performance over baselines for LLaMA-2-7B on RULEmix. |
Knowledge Decoupling via Orthogonal Projection for Lifelong Editing of Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for enhancing large language models (LLMs) have achieved some success, but their knowledge understanding and memory capacity significantly degrades after extensive editing. |
| Approach: | They propose a method that stores the basis vectors of the representation space of past edits in a knowledge cache and projects the gradient of the current edit onto a space orthogonal to previous knowledge for updating. |
| Outcome: | The proposed method improves question-answering ability and hallucination mitigation by 14% and 61% for large language models after 3,000 edits. |
Knowledge Graph-Driven Memory Editing with Directional Interventions (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are hampered by inaccuracies and outdated information. |
| Approach: | They propose a framework that constructs knowledge graphs using available information to guide the direction of knowledge editing. |
| Outcome: | The proposed framework allows consistent, aligned, and stable information during large-scale editing scenarios. |
One for All: Update Parameterized Knowledge Across Multiple Models with Once Edit (2025.acl-long)
Copied to clipboard
Weitao Ma, Xiyuan Du, Xiaocheng Feng, Lei Huang, Yichong Huang, Huiyi Zhang, Xiaoliang Yang, Baohang Li, Xiachong Feng, Ting Liu, Bing Qin
| Challenge: | Existing methods for modifying large language models focus on individual models, resulting in errors and hallucinations. |
| Approach: | They propose an ensemble-based approach that employs a plug-in model as the editing module and a dynamic weight mechanism to enhance its effectiveness. |
| Outcome: | The proposed approach outperforms existing methods while achieving superior editing efficiency. |
EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models (2024.acl-demos)
Copied to clipboard
Peng Wang, Ningyu Zhang, Bozhong Tian, Zekun Xi, Yunzhi Yao, Ziwen Xu, Mengru Wang, Shengyu Mao, Xiaohan Wang, Siyuan Cheng, Kangwei Liu, Yuansheng Ni, Guozhou Zheng, Huajun Chen
| Challenge: | Large Language Models (LLMs) suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data. |
| Approach: | They propose an easy-to-use knowledge editing framework for Large Language Models that allows users to easily edit updated knowledge and adjust undesired behavior while minimizing the impact on unrelated inputs. |
| Outcome: | The proposed framework surpasses traditional fine-tuning in terms of reliability and generalization. |
GeoEdit: Geometric Knowledge Editing for Large Language Models (2025.emnlp-main)
Copied to clipboard
Yujie Feng, Li-Ming Zhan, Zexin Lu, Yongxin Xu, Xu Chu, Yasha Wang, Jiannong Cao, Philip S. Yu, Xiao-Ming Wu
| Challenge: | Existing training-based model editing methods struggle to incorporate new knowledge while preserving unrelated general knowledge. |
| Approach: | They propose a framework that uses geometric relationships to differentiate between neurons associated with new knowledge updates and those related to general knowledge perturbations. |
| Outcome: | The proposed framework avoids updating neurons with directions approximately orthogonal to existing knowledge, thus preserving the model’s generalization ability. |
AdaEdit: Advancing Continuous Knowledge Editing For Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge editing methods that can efficiently update knowledge in LLMs are limited due to budget constraints. |
| Approach: | They propose a method that can enhance the performance of edited LLMs in large-size continuous editing regimes. |
| Outcome: | Extensive empirical evaluations on multiple LLMs show that the proposed method outperforms existing methods without compromising the general abilities of these models. |
MicroEdit: Neuron-level Knowledge Disentanglement and Localization in Lifelong Model Editing (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for enhancing large language models are designed for single or limited edits, lacking the capacity to support long-term, multi-round knowledge updates. |
| Approach: | They propose a neuron-level editing method that performs minimal interventions within large language models (LLMs) by leveraging a sparse autoencoder, MicroEdit disentangles knowledge representations and activates only a minimal set of necessary neurons for precise parameter updates. |
| Outcome: | Extensive experiments show that MicroEdit outperforms prior methods and robustly handles lifelong knowledge editing across QA and Hallucination settings on LLaM and Mistral. |
AlphaEdit+: Model Editing in the Presence of Conflicting and Inconsistent Knowledge (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for knowledge editing struggle with knowledge conflicts and inconsistencies. |
| Approach: | They propose a new method for knowledge editing that relaxes null-space constraints and introduces a weighting scheme to mitigate conflicts between new and historical knowledge. |
| Outcome: | The proposed method outperforms existing methods on challenging datasets and outperformed existing methods. |