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A Comprehensive Study of Knowledge Editing for Large Language Models

Ningyu Zhang, Yunzhi Yao, Bozhong Tian, Peng Wang, Shumin Deng, Mengru Wang, Zekun Xi, Shengyu Mao, Jintian Zhang, Yuansheng Ni, Siyuan Cheng, Ziwen Xu, Xin Xu, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Lei Liang, Zhiqiang Zhang, Xiaowei Zhu, Jun Zhou, Huajun Chen

arXiv:2401.01286v5cs.CLcs.AIcs.CVcs.HCcs.LG

TL;DR

LLMs need efficient post-training updates because retraining is costly while knowledge becomes outdated or undesirable. The paper surveys knowledge editing, proposes a three-part taxonomy, introduces KnowEdit, and analyzes usability and mechanisms. It finds that current methods can update facts with limited disruption, while knowledge conflicts, integration points, and trustworthy sanitization remain challenges.

  • Problem

    LLMs contain outdated or undesirable knowledge, but retraining them to correct such problems is costly and time-consuming.

  • Method

    The paper reviews knowledge editing, classifies methods as external knowledge, model merging, or intrinsic editing, and evaluates them with KnowEdit and extensive analyses.

  • Results

    Current methods can perform factual updates with minimal disruption to general tasks and adaptability across diverse knowledge domains.

  • Takeaways & Limitations

    Knowledge editing can support efficient model adaptation while also revealing how LLM knowledge is stored, manipulated, and accessed.

  • Takeaways & Limitations

    Knowledge conflicts and the optimal point for integrating information remain significant challenges, while effective and verifiable sanitization of sensitive knowledge still needs research.

Abstract

from arXiv · show

Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies in the significant computational demands during training, arising from their extensive parameterization. This challenge is further intensified by the dynamic nature of the world, necessitating frequent updates to LLMs to correct outdated information or integrate new knowledge, thereby ensuring their continued relevance. Note that many applications demand continual model adjustments post-training to address deficiencies or undesirable behaviors. There is an increasing interest in efficient, lightweight methods for on-the-fly model modifications. To this end, recent years have seen a burgeoning in the techniques of knowledge editing for LLMs, which aim to efficiently modify LLMs' behaviors within specific domains while preserving overall performance across various inputs. In this paper, we first define the knowledge editing problem and then provide a comprehensive review of cutting-edge approaches. Drawing inspiration from educational and cognitive research theories, we propose a unified categorization criterion that classifies knowledge editing methods into three groups: resorting to external knowledge, merging knowledge into the model, and editing intrinsic knowledge. Furthermore, we introduce a new benchmark, KnowEdit, for a comprehensive empirical evaluation of representative knowledge editing approaches. Additionally, we provide an in-depth analysis of knowledge location, which can give a deeper understanding of the knowledge structures inherent within LLMs. Finally, we discuss several potential applications of knowledge editing, outlining its broad and impactful implications.

1 Introduction

Knowledge editing addresses costly and outdated LLM knowledge by enabling targeted post-training updates. The paper surveys these methods, organizes them by human-learning analogies, evaluates them with KnowEdit, and analyzes usability and mechanisms.

  • LLMs can retain extensive knowledge but suffer from factual errors, harmful outputs, and outdated information because retraining is costly and time-consuming.Knowledge editing offers cost-effective post-hoc modification while aiming to preserve broader model performance.
  • The paper categorizes knowledge editing into external knowledge, model-internal merging, and intrinsic-knowledge editing, corresponding to recognition, association, and mastery.External approaches expose models to updated knowledge; merging methods connect new and existing representations; intrinsic editing modifies model weights.
  • KnowEdit benchmarks representative approaches across knowledge insertion, modification, and erasure to provide fair empirical comparisons.The study conducts experiments on 12 NLP datasets covering performance, usability, and underlying mechanisms.
  • Current methods can perform factual updates with minimal disruption to general tasks and adaptability across diverse knowledge domains.The paper presents this as evidence of usability in general and multi-task settings.
  • Knowledge-location analysis finds concentration in one or more value-layer columns and often localizes entity-related areas rather than entire factual contexts.The authors note that edited models may recall memorized information or derive answers through multi-step reasoning, while unintended consequences remain possible.
  • The paper discusses applications in efficient machine learning, generated content, trustworthy AI, personalized agents, energy consumption, and interpretability.These applications and broader impacts motivate further research on efficient and innovative LLM adaptation.

2 Background

The background describes Transformer components and the still-mysterious organization and retrieval of knowledge in LLMs. It contrasts knowledge editing with parameter-efficient tuning, augmentation, continual learning, and unlearning.

  • Transformer architecture: Transformers use stacked encoder-decoder blocks containing self-attention and feed-forward neural networks, enabling broad NLP capabilities.The cited architecture passages describe self-attention as contextual processing and FFNs as two linear transformations separated by ReLU.
  • Knowledge storage: LLMs store linguistic, commonsense, arithmetic, and world knowledge in their parameters, but how this knowledge is organized remains largely unknown.Research therefore investigates mechanistic explanations and knowledge storage within Transformer models.
  • Knowledge storage: Prior studies locate linguistic structure across layers and describe FFN layers as key-value memories whose value vectors encode interpretable concepts and knowledge.Other work examines weight-space regions, individual neurons, and information flow to explain storage and retrieval.
  • Related techniques: Parameter-efficient tuning reduces updated parameters and resources, but its effectiveness for knowledge editing remains largely unexplored.PET is usually designed to improve task performance rather than specifically edit stored knowledge.
  • Related techniques: Knowledge augmentation supplies retrieved context for missing or incorrect information, but noisy or poorly related retrieval can cause spurious reasoning and offers limited fine-grained control.Retrieval may operate over broad passages without precisely controlling which model information changes.
  • Related techniques: Knowledge editing is presented as a notable alternative to conventional fine-tuning, which demands substantial time and memory and risks overfitting and catastrophic forgetting.Related techniques also include continual learning for retaining prior knowledge and unlearning for removing undesirable behaviors.

3.1 Preliminary

LLMs function as virtual knowledge stores because training on diverse data equips them with extensive factual and commonsense information used in downstream tasks.

  • Extensive training on diverse datasets gives LLMs substantial factual and commonsense knowledge, positioning them as virtual knowledge stores.This stored knowledge has been applied to numerous downstream tasks.

3.2 Task Definition

Knowledge editing updates selected information in an LLM while aiming to preserve other knowledge. The task includes insertion, modification, and erasure, each serving a distinct update objective.

  • Knowledge editing applies a process F to an original model θ to alter targeted knowledge while preserving the model’s broader knowledge.The task is challenging because knowledge is distributed and entangled in LLMs.
  • Together, insertion, modification, and erasure define essential model-editing settings for transforming, correcting, or ethically adapting LLM behavior.These settings cover adding, changing, and removing model knowledge.
  • Knowledge insertion: Knowledge insertion adds information that was previously outside the model’s knowledge.It addresses the need to incorporate emergent information as fields and entities change.
  • Knowledge modification: Knowledge modification alters knowledge already stored in the model, including amendment of inaccuracies and disruption toward counterfactual responses.The paper identifies correction and counterfactual answering as distinct modification goals.
  • Knowledge erasure: Knowledge erasure removes pre-existing facts, relationships, or attributes from a model to expunge undesirable or confidential information.The formal erasure objective maps existing knowledge to an empty target.

3.3 Methods

The paper organizes knowledge editing around recognition, association, and mastery, corresponding to external knowledge, merged representations, and intrinsic parameter edits. These approaches trade implementation practicality against permanence, scalability, and control of side effects.

  • Unified taxonomy: Knowledge editing methods follow three cognitive phases: recognition exposes models to contextual updates, association connects new and existing knowledge, and mastery integrates knowledge into parameters.The corresponding categories are resorting to external knowledge, merging knowledge into the model, and editing intrinsic knowledge.
  • Recognition Phase: Recognition methods maintain memories and retrieve relevant cases or use auxiliary counterfact models, avoiding changes to the original model.IKE uses copy, update, and retain demonstrations, while SERAC uses a classifier to select a counterfact model.
  • Association Phase: Association methods learn a representation hKnow and merge it with the original representation h, including through added neurons, output heads, or LoRA modules.T-Patcher adds one neuron per output error, CaliNet uses a fixed number of neurons, and MELO dynamically indexes LoRA blocks.
  • Association Phase: Association methods face knowledge conflicts, uncertain integration locations, and potential parameter growth as each edit may require additional capacity.These concerns create challenges for scalability and efficiency.
  • Mastery Phase: Mastery methods directly modify intrinsic parameters through fine-tuning, meta-learning, or locate-and-edit procedures, but can suffer from forgetting, overfitting, or uncontrolled side effects.Direct parameter editing is more permanent, yet the unclear mechanisms of LLMs make side effects difficult to control.

3.4 New Benchmark: KnowEdit

The paper introduces KnowEdit as a comprehensive benchmark for knowledge editing and curates six datasets spanning insertion, modification, and erasure settings. Its selection covers factual, sentiment, hallucination, privacy, and related editing challenges, while acknowledging that some datasets remain unavailable.

  • Benchmark overview: KnowEdit provides a comprehensive evaluation framework for representative knowledge editing techniques.The paper introduces it alongside an overview of existing datasets.
  • Dataset composition: The benchmark curates six datasets covering insertion, modification, and erasure across fact manipulation, sentiment modification, and hallucination generation.The selected datasets are summarized statistically in Table 3.
  • Insertion: WikiDatarecent supports insertion edits by using facts added to Wikidata after July 2022, simulating outdated models encountering new world knowledge.The data are split into training and testing sets.
  • Modification and erasure: Knowledge modification uses ZsRE, WikiBio, Wikidatarecent, and Convsent, while Sanitation targets erasure of privacy-related information stored in language models.Sanitation pairs questions with answers containing information intended to be forgotten.
  • Scope and extensions: The benchmark discussion also covers datasets for internal logical constraints and cross-lingual editing, but some datasets were newly published or unavailable during evaluation.The authors focus on popular available works and plan future expansion.

3.5 Evaluation for Knowledge Editing

The evaluation framework assesses whether edits succeed, transfer to related reasoning tasks, remain local, and preserve fluent generation. These criteria reflect the challenge that changing one fact can affect interconnected knowledge.

  • Evaluation criteria: Knowledge editing is evaluated through edit success, portability, locality, and fluency because changing one fact may affect other interconnected facts.The four categories summarize key criteria from prior work.
  • Edit Success: Edit success requires correct edited answers for both the original context and paraphrased contexts.It combines reliability with generalization to test whether the underlying factual knowledge changes rather than only its expression.
  • Portability: Portability tests whether an edited model handles downstream implications through aliases, compositional reasoning, and logical generalization.For example, changing a president can require changing the answer about the First Lady, while reversed relations should also reflect the edit.
  • Locality: Locality measures whether unrelated knowledge remains unchanged, including in-distribution behavior such as retention of original objects in one-to-many relationships.The framework distinguishes in-distribution locality and notes that overediting is common.
  • Fluency: Fluency uses weighted average bi-gram and tri-gram entropies to measure post-edit generation ability and detect repetitive or over-targeted outputs.The metric follows prior evaluation practice using fluency after editing.

4 Experiments

Experiments evaluate knowledge editing on Llama2-7b-chat across factual editing, general-task, cross-domain, sequential-editing, and error-analysis settings. FT-M performs best overall, but portability remains weak and method-specific trade-offs emerge across locality, fluency, generalization, and scalability.

  • Experiment Settings: Experiments use Llama2-7b-chat, eight established editing methods, and EasyEdit to evaluate post-editing performance across multiple tasks.Evaluation covers edit success, portability, locality, fluency, general ability, cross-domain editing, sequential editing, and error patterns.
  • Main Results: FT-M achieves the strongest overall performance, while all current methods exhibit low portability.The reported comparison attributes FT-M’s advantage to fine-tuning specific parameters, while identifying portability as a shared weakness.
  • Main Results: SERAC performs strongly on knowledge insertion and modification, with good locality except on ZsRE, but its smaller counterfact model reduces fluency.IKE requires long concatenated demonstrations, limiting downstream-task use, and performs less effectively on ICE.
  • Main Results: MEND provides broadly strong parameter-editing results, whereas ROME and MEMIT trade higher edit success for weaker locality and portability.FT-L has lower edit success than ROME or MEMIT but better locality and portability; Convsent edit success remains below 65% across current methods.
  • Multi-Task Knowledge Editing: MEND performs better on reasoning portability and AdaLoRA shows stronger logical generalization, while cross-domain results favor ZsRE-trained MEND and SERAC’s cache.Sequential editing by FT-L, ROME, and AdaLoRA suffers a dramatic drop across metrics after 1,000 edits.
  • Impact on General Tasks: After five targeted factual edits, general benchmark performance remains close to unedited models, although FT-L drops on TriviaQA from 45.39 to 34.60.The reported results indicate limited disruption overall, with one notable exception on TriviaQA.
  • Multi-Task Knowledge Editing: RASE stores edit operations in memory and retrieves them individually, addressing the mismatch between continuously changed models and algorithms based on the original model.The approach combines retrieval augmentation with editing rather than continuously modifying model parameters.
  • Error and Case Analysis: Partial token replacement is the main ZsRE error, reflecting conflict between original and target knowledge; FT-L produces fact-irrelevant words in 47.3% of cases.SERAC often generates meaningless tokens, AdaLoRA may miss target-related tokens, and some methods either repeat text or retain the original answer.

5 Analysis

The analysis examines why knowledge-editing methods differ, how edits alter model weights, and how reliably knowledge can be located. It finds sparse method-specific updates, uneven post-edit retrieval gains, and unresolved limits in tracing factual knowledge and reasoning.

  • Current research leaves the reasons for editing methods’ superior performance, the comparison with fine-tuning, and knowledge-location efficacy insufficiently explored.
  • Weight-update mechanisms: ROME, MEND, and MEMIT produce sparse weight updates, whereas fine-tuning distributes modifications more uniformly across weights.ROME and MEMIT concentrate changes in one or several value-layer columns.
  • Embedding-space analysis: MEND exceeds 90% Hit@1 after editing, while MEMIT’s Hit@50 rises from 59.7% to 70.2%; ROME and FT-L show no performance increase.
  • Limitations: Knowledge editing does not necessarily make factual knowledge usable during reasoning, and existing localization methods struggle to balance erasing target sequences with retaining other memorized data.
  • Knowledge locating: RSim falls below 0.6 beyond five layers, indicating little difference between locating unrelated knowledge and related knowledge chains.The analysis computes similarity from overlap between identified layers using consistency and relevance datasets.
  • Knowledge locating: Causal analysis can locate areas related to an entity without locating the whole fact, leaving memorization versus multi-step reasoning unresolved.In the SMAP case study, related and unrelated facts appear possibly connected around five layers.

6 Applications

Knowledge editing is being applied across question answering, code and graph models, multimodal systems, trustworthy AI, and personalized agents. These applications show broad promise, but robust downstream integration, semantic safety, bias mitigation, and multimodal evaluation remain open challenges.

  • Model Updating: Knowledge editing has been integrated into question answering, fact checking, and natural language generation, but translating edits into robust task improvements remains an open challenge.The paper identifies online inference, complex reasoning, and tight integration with downstream architectures and objectives as unresolved issues.
  • Model Updating: 44.46% to 48.54%: MedEdit improved medical question-answering accuracy using knowledge editing.
  • Model Manipulation: Knowledge editing has also been applied to code generation, knowledge graph embeddings, graph unlearning, and model manipulation through distillation, pruning, and knowledge transfer.
  • Multimodal Applications: Multimodal editing can modify generated content, but evaluating cross-modal coherence and aligning multimodal representations require new benchmarks and metrics.
  • Trustworthy AI: Knowledge editing supports trustworthy AI by targeting toxic, harmful, biased, or otherwise undesirable model behaviors while modifying only selected parameters.
  • Trustworthy AI: Token-level safety edits may remove sensitive terminology and its associated perspectives, motivating semantically safe generation and conceptual editing across adversarial inputs.
  • Trustworthy AI: Bias mitigation remains difficult because complex semantic, pragmatic, and commonsense biases may evade benchmarks, while systemic training-data biases require broader strategies.
  • Personalized Agents: Personalized knowledge editing can align LLMs with user preferences in economic business, dialogue, and recommendation systems, supporting improved goods and services and user satisfaction.

7 Discussion and Conclusion

The paper highlights unresolved questions about whether edits are meaningful, how widely their effects spread, and how models can keep pace with changing knowledge. It nevertheless presents knowledge editing as worth pursuing because LLM stochasticity also supports creativity and adaptability.

  • Open Challenges: Current editing methods may alter output probabilities or prompt responses without establishing that the resulting changes are meaningful or intentional knowledge edits.
  • Open Challenges: Assessing an edit’s effects on other capabilities is difficult because information and skills are interwoven within language models, favoring task-specific or domain-specific applications.
  • Open Challenges: LLMs must incorporate continuously changing knowledge while adapting their reasoning, actions, and communication methods, requiring more agile control approaches.
  • Discussion: The stochastic nature of LLMs contributes both complexity and creativity, so the paper argues that knowledge editing remains worth exploring.

Broader Impacts

Knowledge editing is framed as an efficient way to update and refine LLMs while also exposing how their knowledge is stored, manipulated, and accessed. Its broader implications include accessibility, fairness, security, and interpretability.

  • Scope and Significance: Knowledge editing updates and refines LLMs to support continuous improvement, adaptation, and alignment with desired objectives and values.
  • Scope and Significance: Despite variation in method effectiveness, editing can deepen understanding of how LLM knowledge is stored, manipulated, and accessed, enhancing interpretability and transparency.
  • Broader Impacts: Knowledge editing may affect model accessibility, fairness, security, and interpretability beyond improving efficiency.

Contributions

The paper’s contributions involved initiating and organizing the research, drafting sections, constructing the benchmark, conducting experiments, and reviewing the manuscript.

  • Author Contributions: The authors contributed to research organization, section drafting, benchmark construction, experiments, organization suggestions, and proofreading.
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