A Comprehensive Study of Knowledge Editing for Large Language Models

Motivation

• Knowledge editing enables cost-effective post-hoc correction of factual errors, harmful content, and outdated information without retraining the entire model.The methods target specific adjustments while preserving overall performance, supporting fairness and safety in AI applications.Source: §1 · p. 3

Taxonomy

• The paper categorizes knowledge editing into resorting to external knowledge, merging knowledge into the model, and editing intrinsic knowledge, based on educational and cognitive theories.These correspond respectively to recognition through contextual exposure, association through connections with existing knowledge, and mastery through parameter modification.Source: §1 · p. 3

Benchmark

• KnowEdit benchmarks cutting-edge methods across knowledge insertion, modification, and erasure, providing fair empirical comparisons of their overall performance.The paper conducts experiments on 12 NLP datasets to evaluate performance, usability, and underlying mechanisms.Source: §1 · p. 4

Usability

• Experiments indicate that contemporary editing methods update facts with minimal disruption to general tasks and retain adaptability across diverse knowledge domains.The paper also examines multi-task knowledge editing and its effects on general capabilities.Source: §1 · p. 4

Mechanism

• Mechanistic analyses find pronounced editing in one or several value-layer columns, while knowledge location often identifies entity-related areas rather than the full factual context.The paper therefore considers whether edited models recall memorized information or derive answers through multi-step reasoning, alongside possible unintended consequences.Source: §1 · p. 4

Applications and impact

• The paper examines applications in efficient machine learning, AIGC, trustworthy AI, and personalized agents, while discussing energy consumption, interpretability, and public research resources.Tools, code, data splits, and trained model checkpoints are intended to be publicly accessible.Source: §1 · p. 4

2.1.1 Transformers for LLM

• Transformers comprise stacked encoder and decoder layers with self-attention and feed-forward networks, while decoder blocks additionally use cross-attention to integrate encoder information.Self-attention lets each position attend to all positions in the preceding layer; the FFN applies two linear transformations with an intervening ReLU activation.Source: §2.1.1 · pp. 4–5

2.1.2 Mechanism of Knowledge Storage in LLMs

• Transformer parameters encode linguistic, commonsense, arithmetic, and world knowledge, but how this knowledge is organized and retrieved remains an open research problem.Studies variously associate knowledge with hierarchical layers, FFN key-value memories, localized neurons, early-layer FFNs, weight-space regions, attention-based attribute extraction, and linear relational or spatial representations, while cautioning that correlations may not establish storage mechanisms.Source: §2.1.2 · pp. 5–6, Figure 1 · p. 5

2.2 Related Techniques

• Parameter-efficient tuning reduces adaptation costs by updating few parameters through addition-based, specification-based, or reparameterization-based methods, including Adapters and LoRA.These methods aim to preserve performance while reducing required resources and tuning time, but they are generally designed for task enhancement rather than knowledge editing specifically.Source: §2.2 · p. 6
• Retrieval augmentation supplies missing knowledge through retrieved context or representations, but noisy retrieval and coarse passage-level control can cause spurious reasoning and degraded performance.Retrieved information may be integrated at input, intermediate, or output layers, yet retrieval does not provide fine-grained control over which internal information is modified.Source: §2.2 · p. 6
• Continual learning preserves earlier knowledge while acquiring new tasks, whereas machine unlearning removes undesirable behavior; knowledge editing instead precisely refines or erases internal knowledge independently of tasks or domains.Knowledge editing is presented as a granular alternative to broad retraining or fine-tuning, addressing the fixedness of deployed pretrained language models.Source: §2.2 · p. 7

3.1 Preliminary

• LLMs store extensive factual and commonsense information acquired through substantial training on diverse datasets [148] [149].Source: §3.1 · p. 8
• This stored knowledge supports numerous downstream tasks [150].Source: §3.1 · p. 8
• Wang et al. showed that LLMs can autonomously construct high-quality knowledge graphs without human supervision.Source: §3.1 · p. 8
• Despite these capabilities, LLMs can produce inaccurate or erroneous outputs in practical applications.Source: §3.1 · p. 8
• Knowledge editing is introduced to provide quick and precise modifications that correct errors and improve LLM output accuracy and relevance.Source: §3.1 · p. 8
• The approach aims to make LLMs more reliable and adaptable as dynamic knowledge bases for applications.Source: §3.1 · p. 8

Task Definition

• Knowledge editing applies F to an original model θ to override beliefs about targeted knowledge k while keeping other knowledge intact.The goal is to improve consistency and performance without fine-tuning the entire model, across facts, commonsense, and sentiment.Source: §3.2 · p. 8

Knowledge Insertion

• Knowledge insertion adds emergent information previously outside the LLM’s knowledge as fields and entities progress.Source: §3.2 · p. 8

Knowledge Modification

• Knowledge modification alters stored knowledge through amendment of inaccuracies or disruption of factual beliefs to produce counterfactual answers.Disruption is more challenging because counterfactual notions initially receive lower scores than factual knowledge, requiring targeted modification.Source: §3.2 · p. 8

Knowledge Erasure

• Knowledge erasure removes pre-existing facts, relationships, or attributes from the model through the transformation θ′ = F(θ, {k} →{∅}).It can expunge biases and harmful knowledge while limiting recall of confidential or private data.Source: §3.2 · p. 9

Task Definition

• Together, insertion, modification, and erasure enable LLMs to transform, self-correct, and adapt their knowledge as needed.Source: §3.2 · p. 9

3.3 Methods

• Human-inspired knowledge editing comprises recognition, association, and mastery phases, corresponding to external retrieval, representation merging, and direct parameter changes.This framework draws on educational and cognitive research and is summarized in Table 2.Source: §3.3 · p. 9, Table 2 · p. 10

3.3.1 Recognition Phase: Resorting to External Knowledge

• Recognition methods retrieve contextual demonstrations or counterfacts from external memory, including IKE, MeLLo [155], PokeMQA, and SERAC [153].They avoid modifying the original model but remain vulnerable to retrieval errors, harmful content, and knowledge conflicts.Source: §3.3.1 · p. 10

3.3.2 Association Phase: Merge the Knowledge into the Model

• Association methods merge learned knowledge representations with existing model representations through output heads, FFN neurons, LoRA, affine substitutions, or codebooks.Examples include T-Patcher [161], CaliNet [160], MELO, REMEDI, and GRACE [163]; conflicts, integration location, and parameter growth remain challenges, motivating F-Learning [181].Source: §3.3.2 · p. 11

3.3.3 Mastery Phase: Editing Intrinsic Knowledge

• Mastery methods directly encode edits in model parameters using constrained fine-tuning, meta-learning, or locate-and-edit techniques, making changes more permanent but risking forgetting, overfitting, conflicts, and uncontrolled side effects.Meta-learning methods include KE [168], SLAG, MEND [170], MoEditor, ProEditor, and MALMEN [173], while locate-and-edit methods include Knowledge Neuron [39], ROME [79], MEMIT, PMET, BIRD, and AlphaEdit [175].Source: §3.3.3 · pp. 11–12

Benchmark scope

• KnowEdit3 evaluates knowledge editing across six datasets spanning fact manipulation, sentiment modification, hallucination generation, and knowledge erasure.Table 3 summarizes the selected datasets and their knowledge types for insertion, modification, and erasure settings.Source: §3.4 · pp. 12–13, Table 3 · p. 12

Knowledge insertion

• WikiDatarecent simulates insertion of recently added Wikidata facts into models trained before those facts existed, using original training and testing splits.The dataset focuses on triplets inserted after July 2022, representing outdated models encountering new world knowledge.Source: §3.4 · p. 13

Knowledge modification

• Modification evaluation uses ZsRE, WikiBio [163], Wikidatarecent, and ConvSent [153] to test factual answers, hallucination correction, counterfactual knowledge, and sentiment changes.The settings include portability and locality tests for ZsRE, inaccurate-sentence replacement for WikiBio, popular-entity counterfactuals, and topic-specific sentiment editing in ConvSent.Source: §3.4 · p. 13

Knowledge erasure

• Sanitation [188] evaluates knowledge erasure by requiring models to forget sensitive answers while retaining unrelated behavior.Its forgot set measures removal success, while its retain set assesses locality and predefined safe responses help prevent information leakage.Source: §3.4 · p. 13

Related datasets and scope

• The benchmark discussion situates KnowEdit among datasets for logical constraints, cross-lingual, entity-propagation, temporal, commonsense, relation, debiasing, and reasoning-error editing, while noting availability limits and planned expansion.Examples include DepEdit, Eval-KLLM, Bi-ZsRE, ReMaKE and MzsRE, ENTITY INFERENCES, TEMPLAMA, ATOKE [194], MEMITCSK, RaKE [195], and DUNE [196].Source: §3.4 · pp. 13–14

Evaluation Framework

• The framework organizes evaluation around four criteria because knowledge is interconnected and edits may ripple into other facts.Some prior work uses Specificity as another term for locality.Source: §3.5 · pp. 14–15

Edit Success

• Edit success measures whether post-edit models produce the target answer for both original and paraphrased contexts, covering reliability and generalization [170].The criterion targets modification of underlying factual knowledge rather than merely changing its expression.Source: §3.5 · p. 14

Portability

• Portability evaluates whether edits support downstream use through alias handling, compositional reasoning, and logically related changes [69] [155].Examples include recognizing subject aliases, updating implications such as related roles, and changing reversed relations after a fact changes [69].Source: §3.5 · pp. 14–15

Locality

• Locality measures whether editing changes only intended knowledge, testing in-distribution retention and specificity alongside out-of-distribution task performance.In-distribution tests cover forgetfulness and relation specificity, while out-of-distribution evaluation checks whether general NLP abilities remain intact.Source: §3.5 · p. 15

Generative Capacity

• Fluency measures post-edit generative capacity using weighted average bi-gram and tri-gram entropies, with lower values indicating more repetitive text.This addresses models that repeatedly generate edited targets when encountering subject words, following ROME [79].Source: §3.5 · p. 15

4.2 Main Results

• FT-M outperforms other methods overall across knowledge-editing tasks, while all methods show low portability; SERAC excels at insertion and modification but sacrifices fluency, and ICE suffers from knowledge conflicts.MEND performs broadly well, whereas ROME and MEMIT trade stronger edit success for weaker locality and portability. Current methods also struggle on Convsent and Sanitation, with ROME reaching 90% erasure accuracy but only 55.61% locality.Source: §4.2 · pp. 16–17

4.3 Impact on General Tasks

• After five factual edits, edited models generally retain performance close to unedited counterparts on broad benchmarks, except FT-L on TriviaQA, which drops from 45.39 to 34.60.Evaluations used zero-shot CommonsenseQA, PIQA, Xsum, TriviaQA, MMLU, and AGIEval with OpenCompass; the results suggest limited disruption outside directly altered topics.Source: §4.3 · pp. 17–18, Table 5 · p. 18

4.4 Multi-Task Knowledge Editing

• Cross-domain editing favors MEND trained on ZsRE over its recent-data variant, while SERAC’s cache provides strong cross-domain editing performance.The larger ZsRE dataset is attributed to improving MEND’s hyper-network parameter-editing capabilities.Source: §4.4 · p. 18
• FT-L, ROME, and AdaLoRA all suffer dramatic metric declines after 1,000 mixed-domain sequential edits, although AdaLoRA remains stable for about 100 edits.The authors attribute degradation partly to methods repeatedly editing fixed parameter regions that may not store the relevant knowledge; RASE instead stores edit operations in memory for retrieval.Source: §4.4 · p. 19, Figure 4 · p. 19

4.5 Error and Case Analysis

• The four identified failure modes are meaningless-token generation, missing-token generation, knowledge-irrelevant generation, and partial token replacement, with partial replacement dominant because original and target knowledge conflict.On ZsRE, fact-irrelevant words are FT-L’s most common error at 47.3%; SERAC produces meaningless tokens, while AdaLoRA may omit target-related tokens.Source: §4.5 · p. 20
• In a Queen Amina Statue case, IKE, MEND, and ROME successfully changed bronze to limestone fluently, whereas SERAC and FT-L generated repetition or meaningless entities and AdaLoRA retained “bronze”.The case analysis illustrates that factual success can coexist with degraded generation quality or failure to replace the original answer.Source: §4.5 · p. 20, Table 7 · p. 20

5 Analysis

• The analysis investigates knowledge editing, model fine-tuning, knowledge location, and internal knowledge structure because the reasons for editing methods’ performance and their portability remain insufficiently understood.It aims to clarify editing mechanisms and knowledge organization in LLMs, while motivating further study of how knowledge is represented and used.Source: §5 · pp. 20–21

5.1 Comparison of Different Knowledge Editing Methods

• ROME, MEND, and MEMIT produce sparse parameter updates, whereas fine-tuning changes weights more uniformly; ROME and MEMIT concentrate changes in value-layer columns.The value layer’s role in correlated knowledge is consistent with earlier research [42], while MEND’s hypernetwork may serve as a probe of knowledge encoding.Source: Figure 6 · p. 21, §5.1 · p. 22
• MEND achieves over 90% Hit@1 after editing and exceeds 90% Hit@50 before editing, while MEMIT’s Hit@50 rises from 59.7% to 70.2%; ROME and FT-L show no performance increase.These results suggest MEND can identify target neurons without full knowledge-location analysis, whereas the mechanisms and limitations of ROME and FT-L require further investigation.Source: §5.1 · p. 22, Figure 7 · p. 22

5.2 Analysis of Knowledge Locating

• Integrated gradients, SLIMMING, and HARD CONCRETE show strong localization, but DEL reveals difficulty erasing target sequences while preserving other memorized data.For ROME’s causal analysis, RSim falls below 0.6 beyond five layers for both consistency and relevance, suggesting limited distinction between related and unrelated knowledge.Source: §5.2 · p. 23

5.3 The Implicit Knowledge Structure in LLMs

• LLM knowledge is distributed and entangled across parameters and layers rather than cleanly represented as knowledge-graph triples, causing edits to propagate, conflict, or distort other knowledge.Current methods can provide plug-and-play, reversible modifications, but precise interventions require a more comprehensive understanding of model mechanisms.Source: §5.3 · p. 24

6.1 Efficient Machine Learning

• Knowledge editing supports question answering, fact checking, generation, code, graph models, and model manipulation, but robust downstream integration and complex-reasoning edits remain open challenges.Applications include multi-hop retrieval and editing, parameter updates, medical question answering, toxicity detection, hallucination reduction, graph unlearning, pruning, distillation, and model stitching.Source: §6.1 · pp. 24–26

6.2 AI-Generated Content (AIGC)

• Knowledge editing controls multimodal content through VisualQA, image captioning, text-to-image factual editing, multimodal-neuron analysis, and permanent concept erasure, but cross-modal coherence needs better benchmarks and metrics.Erasing concepts from model weights is presented as more permanent than modifying outputs only at inference time.Source: §6.2 · p. 26

6.3 Trustworthy AI

• Knowledge editing targets trustworthy AI by detoxifying, debiasing, defending privacy, and supporting watermarking, while social behavior remains complex and subjective.Methods address toxic generation, gender bias, private-data leakage, multimodal privacy, federated unlearning, and copyright protection.Source: §6.3 · pp. 27–28
• Safety edits often operate at the token level, risking loss of sensitive terminology and related perspectives; conceptual editing is needed for semantically safe responses across adversarial inputs.Bias mitigation is also limited by complex semantic, pragmatic, and commonsense biases and by systemic biases rooted in training data.Source: §6.3 · pp. 27–28

6.4 Human-Computer Interaction: Personalized Agents

• Knowledge editing enables personalized LLM agents whose personalities, opinions, emotions, and recommendations align with individual users across dialogue, business, and recommendation systems.Such personalization is intended to improve user experience, customer satisfaction, and business outcomes.Source: §6.4 · pp. 28–29

• The study highlights present-day knowledge-editing challenges and introduces a benchmark for diverse editing tasks.Current methods show efficacy in certain areas, but significant issues remain for improvement.Source: §7 · p. 29
• Because Transformer mechanisms remain opaque, changing output probabilities or prompt responses may not constitute meaningful or intentional knowledge editing.The next-token prediction foundation makes it unclear whether current edits achieve useful behavioral changes.Source: §7 · p. 29
• The influence boundaries of edits are difficult to assess because language-model information and skills are interwoven, favoring task-specific or domain-specific applications.Such contexts make edited effects more predictable and containable across other capabilities.Source: §7 · p. 29
• Rapidly evolving knowledge requires LLMs to update not only information but also reasoning, actions, and communication while maintaining societal safety.The paper calls for more agile and responsive control that can keep pace with changing information and societal norms.Source: §7 · p. 29
• Knowledge editing remains promising because stochasticity supports creativity and adaptability, but responsible LLMs will require editing combined with security, openness, and accountability.This shift from traditional fine-tuning reflects more specialized and sophisticated model adaptation shaped by prior knowledge, experience, culture, and social interaction; Pinter and Elhadad emphasize stochasticity’s role.Source: §7 · p. 29

• Knowledge editing updates and refines LLMs efficiently, helping them remain current, accurate, and aligned with desired objectives and values.Source: Broader Impacts · p. 30
• Despite variable effectiveness across methods, knowledge editing can reveal how LLM knowledge is stored, manipulated, and accessed, enhancing interpretability, transparency, usability, and trust.Source: Broader Impacts · p. 30
• Knowledge editing may reshape AI and machine learning by advancing model accessibility, fairness, security, and interpretability beyond efficiency improvements.Source: Broader Impacts · p. 30

• The research was initiated and organized by Ningyu Zhang, Yunzhi Yao, Peng Wang, Bozhong Tian, and Shumin Deng.Source: Contributions · p. 30
• Authors contributed to drafting Sections 1–7, benchmark construction, and experiments.Source: Contributions · p. 30
• Additional contributors advised the project, suggested the empirical study, proofread the paper, and provided computation resources.Source: Contributions · p. 30