Papers with Knowledge editing
Aligning Language Models with Real-time Knowledge Editing (2026.acl-long)
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| Challenge: | Mainstream knowledge editing methods are static and fail to keep pace with the evolving real-world knowledge. |
| Approach: | They propose a new paradigm for knowledge editing that integrates edit augmentation and self-adaptive post-alignment inference into CRAFT to improve edit success. |
| Outcome: | The proposed method shows significant performance gain on CRAFT and traditional datasets compared to existing methods. |
Related Knowledge Perturbation Matters: Rethinking Multiple Pieces of Knowledge Editing in Same-Subject (2025.naacl-short)
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Zenghao Duan, Wenbin Duan, Zhiyi Yin, Yinghan Shen, Shaoling Jing, Jie Zhang, Huawei Shen, Xueqi Cheng
| Challenge: | Existing knowledge editing methods struggle when tasked with editing multiple related knowledge pieces for the same subject. |
| Approach: | They propose a benchmark to assess the effectiveness of knowledge editing methods . they use same-subject edits to ensure comprehensive updates to entity-centric knowledge . |
| Outcome: | The proposed method over-relys on subject information, neglecting other critical factors, resulting in reduced editing effectiveness. |
Efficient Knowledge Editing via Minimal Precomputation (2025.acl-short)
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| Challenge: | Knowledge editing methods like MEMIT require a one-time but significant computational cost. |
| Approach: | They propose to pre-compute 44 million hidden vectors per edited layer . authors show that this precomputation step is unnecessary . |
| Outcome: | The proposed methods can be performed by pre-computing a small portion of 44 million hidden vectors. |
The Model Agreed, But Didn’t Learn: Diagnosing Surface Compliance in Large Language Models (2026.findings-acl)
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| Challenge: | Large Language Models internalize vast world knowledge as parametric memory, yet inherit the staleness and errors of their source corpora. |
| Approach: | They propose a framework that subjects models to discriminative self-assessment under diverse contextual pressures to scrutinize subtle behavioral nuances induced by memory modifications. |
| Outcome: | The proposed framework achieves high benchmarks without overwriting internal beliefs, while recursive modifications accumulate representational residues, triggering cognitive instability and permanently diminishing the reversibility of the model’s memory state. |
WilKE: Wise-Layer Knowledge Editor for Lifelong Knowledge Editing (2024.findings-acl)
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| Challenge: | Existing knowledge editing methods focus on single editing, failing to meet the requirements for lifelong editing. |
| Approach: | They propose an approach that selects editing layer based on the pattern matching degree of editing knowledge across different layers in language models. |
| Outcome: | The proposed method improves on GPT2-XL and GPT-J in lifelong editing compared to state-of-the-art methods . |
AdaEdit: Advancing Continuous Knowledge Editing For Large Language Models (2025.acl-long)
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| 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. |
Beyond Memorization: A Rigorous Evaluation Framework for Medical Knowledge Editing (2026.eacl-long)
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| Challenge: | Existing knowledge editing methods show promising results on general-domain benchmarks, but their effectiveness in the medical domain remains largely unexplored. |
| Approach: | They propose a framework to evaluate medical knowledge editing using model-generated rationales as editing targets. |
| Outcome: | The proposed method improves editing efficacy and generalization in medical models without full retraining. |
StepKE: Stepwise Knowledge Editing for Multi-Hop Question Answering (2025.findings-emnlp)
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| Challenge: | Existing knowledge editing methods overlook interplay with pre-existing knowledge, leading to inconsistent edit propagation. |
| Approach: | stepKE integrates edited and existing knowledge for coherent multi-hop reasoning . stepKE decomposes multi-step questions into sequential single-hop sub-questions . |
| Outcome: | Experiments show that StepKE generates more accurate and consistent responses than baselines. |
Context-Robust Knowledge Editing for Language Models (2025.findings-acl)
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| Challenge: | Existing knowledge editing methods assess success by considering only edited knowledge without preceding contexts. |
| Approach: | They propose a method to strengthen context robustness by minimizing context-sensitive variance in hidden states of the model. |
| Outcome: | The proposed method improves the success rate in situations where a preceding context is present and preserves the overall capabilities of the model. |
Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models (2024.findings-emnlp)
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| Challenge: | Knowledge editing is a promising technique for updating factual knowledge in large language models (LLMs) but studies have identified side effects such as knowledge distortion and the deterioration of general abilities that have emerged after editing. |
| Approach: | They propose to evaluate the side effects of knowledge editing in large language models using metrics and benchmarks. |
| Outcome: | The results of the study highlight the limitations of current knowledge editing methods and outline potential research directions. |
STEAM: A Semantic-Level Knowledge Editing Framework for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods for locate-and-editing focus on token-level likelihood optimization without addressing semantic coherence. |
| Approach: | They propose a semantic-level knowledge editing framework that enhances integration of updated knowledge into the model's knowledge structure. |
| Outcome: | The proposed framework improves integration of updated knowledge into the model's knowledge structure and improves semantic coherence. |
MindBridge: Scalable and Cross-Model Knowledge Editing via Memory-Augmented Modality (2025.findings-acl)
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| Challenge: | Existing knowledge editing methods overfit to specific models, causing edited knowledge to be discarded during each LLM update and requiring frequent re-editing. |
| Approach: | They propose a solution that allows editors to edit knowledge in multiple LLMs at the same time. |
| Outcome: | The proposed solution performs better even in editing tens of thousands of knowledge entries and can adapt to different LLMs. |
Cross-Lingual Knowledge Editing in Large Language Models (2024.acl-long)
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| Challenge: | Knowledge editing is a promising technique to adapt large language models to new knowledge without retraining from scratch. |
| Approach: | They propose to use a multilingual dataset to translate a large-scale cross-lingual synthetic dataset from English to Chinese and then to evaluate their performance in Chinese. |
| Outcome: | The proposed method can change model performance on several special cases without retraining from scratch. |
AlphaEdit+: Model Editing in the Presence of Conflicting and Inconsistent Knowledge (2026.findings-acl)
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| 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. |
One for All: Update Parameterized Knowledge Across Multiple Models with Once Edit (2025.acl-long)
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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. |
AKEW: Assessing Knowledge Editing in the Wild (2024.emnlp-main)
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| Challenge: | Recent Large Language Models (LLMs) have revolutionized the NLP field but their knowledge could become incorrect or outdated over time. |
| Approach: | They propose a new practical benchmark for knowledge editing that covers three editing settings of knowledge updates: structured facts, unstructured texts as facts, and extracted triplets. |
| Outcome: | The proposed method covers structured facts, unstructured texts as facts, and extracted triplets. |
Revealing the Deceptiveness of Knowledge Editing: A Mechanistic Analysis of Superficial Editing (2025.acl-long)
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| Challenge: | Existing knowledge editing algorithms are prone to generating original knowledge . despite the fact that many models achieve near-perfect performance, superficial editing remains a challenge . |
| Approach: | They propose to use "**superficial editing**" to describe the phenomenon . they investigate the internal mechanisms of the attention module and their corresponding left singular vectors . |
| Outcome: | The proposed method can modify specific knowledge in a pretrained large language model while ensuring that unrelated knowledge remains unaffected. |
TAXI: Evaluating Categorical Knowledge Editing for Language Models (2024.findings-acl)
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| Challenge: | Knowledge editing aims to inject new facts into language models to improve factuality, but current benchmarks fail to evaluate consistency, which is critical to ensure efficient, accurate, and generalizable edits. |
| Approach: | They manually create a new benchmark dataset specifically created to evaluate consistency in categorical knowledge edits. |
| Outcome: | The results show that the editors achieve marginal, yet non-random consistency, and their consistency far underperforms human baselines. |
Fisher-Driven Adaptive Locating for Knowledge Editing in Large Language Models (2026.acl-long)
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| Challenge: | Existing methods for locating and editing static knowledge are costly and risk catastrophic forgetting or error. |
| Approach: | They propose a Fisher-driven adaptation-aware locating strategy that dynamically identifies which model components should be edited for a given knowledge update. |
| Outcome: | Experiments on standard benchmarks show that FiDAL improves editing effectiveness and knowledge preservation across multiple editing methods. |
TamEdit: Trajectory-Aware Meta-Learning for Specificity-Preserving Continual Knowledge Editing (2026.acl-long)
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| Challenge: | Existing methods for continual knowledge editing focus on single edits or preventing knowledge forgetting. |
| Approach: | They propose a meta-learning method that preserves specificity for continual knowledge editing by capturing relationships between different single edits within the trajectory. |
| Outcome: | Experiments show that TamEdit outperforms baselines in continual editing while preserving general capabilities. |
Edit Once, Update Everywhere: A Simple Framework for Cross-Lingual Knowledge Synchronization in LLMs (2025.findings-acl)
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| Challenge: | Existing methods to update large language models focus on single-language editing or basic multilingual editing, failing to achieve true cross-linguistic knowledge synchronization. |
| Approach: | They propose a cross-linguistic knowledge democracy edit technique to improve cross-lingual performance. |
| Outcome: | The proposed method improves cross-lingual performance while maintaining high accuracy in monolingual settings. |
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%. |
Decoupling Reasoning and Knowledge Injection for In-Context Knowledge Editing (2025.findings-acl)
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| Challenge: | Existing knowledge editing approaches directly edit model context without isolating target knowledge from the reasoning path of model inference, resulting in unreliable and low-quality outputs, especially in multi-hop tasks. |
| Approach: | They propose a framework that separates model reasoning from knowledge editing and propose 'DecKER' that allows users to modify specific factual associations without retraining the entire model. |
| Outcome: | The proposed framework significantly improves multi-hop reasoning performance by mitigating knowledge conflicts and preserving reasoning integrity. |
KELE: Residual Knowledge Erasure for Enhanced Multi-hop Reasoning in Knowledge Editing (2025.findings-emnlp)
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| Challenge: | Existing knowledge editing techniques show limitations when applied to multi-hop reasoning . residual single-hop knowledge causes edited models to revert to original answers . |
| Approach: | They propose a knowledge editing method that incorporates a Knowledge Erasure mechanism for Large language model Editing (KELE) they propose an erasure function for residual knowledge and an injection function for new knowledge . |
| Outcome: | The proposed method significantly improves multi-hop reasoning capability of edited models. |
Can We Edit LLMs for Long-Tail Biomedical Knowledge? (2025.findings-emnlp)
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| Challenge: | Existing knowledge editing methods can enhance LLMs' performance on long-tail biomedical knowledge, but their performance on high-frequency popular knowledge remains inferior to that on high frequency popular knowledge. |
| Approach: | They conduct the first comprehensive study to investigate the effectiveness of knowledge editing methods for editing long-tail biomedical knowledge. |
| Outcome: | The proposed methods improve LLMs' performance on long-tail biomedical knowledge, but their performance on high-frequency popular knowledge remains inferior even after editing. |
Orthogonal Representation Editing: Decoupling Semantic Entanglement in Batch Knowledge Editing of LLMs (2026.findings-acl)
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| Challenge: | Existing knowledge editing methods suffer from performance degradation in batch knowledge editing. |
| Approach: | They propose an orthogonal representation editing method which decouples semantic entanglement from edit vectors and enforcing orthogonals on edit vector. |
| Outcome: | The proposed method outperforms existing methods and achieves superior performance in cross-lingual knowledge editing scenarios. |