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Editing Large Language Models: Problems, Methods, and Opportunities
Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang
TL;DR
Maintaining LLM relevance and correcting errors requires efficient behavioral updates that avoid unwanted effects on other inputs. The paper defines model-editing objectives, systematically compares methods across models and editing settings, and introduces broader evaluation dimensions. It finds substantial capacity for factual editing but persistent limitations in portability, locality, and efficiency, constraining practical application.
Problem
Strategies for keeping LLMs relevant and fixing their errors without retraining a wholly new model, while avoiding effects on other inputs, remain unclear.
Method
The paper standardizes model-editing definitions, compares methods under controlled conditions across datasets and models, and evaluates portability, locality, and efficiency.
Results
Current methods show considerable capacity for factual model editing but are limited in portability, locality, and efficiency.
Takeaways & Limitations
Systematic evaluation helps researchers choose appropriate editing techniques while highlighting open issues requiring further research.
Takeaways & Limitations
Experiments cover models up to 20B, and effectiveness for architectures such as Llama remains unexplored.
Abstract
from arXiv · showhide
Despite the ability to train capable LLMs, the methodology for maintaining their relevancy and rectifying errors remains elusive. To this end, the past few years have witnessed a surge in techniques for editing LLMs, the objective of which is to efficiently alter the behavior of LLMs within a specific domain without negatively impacting performance across other inputs. This paper embarks on a deep exploration of the problems, methods, and opportunities related to model editing for LLMs. In particular, we provide an exhaustive overview of the task definition and challenges associated with model editing, along with an in-depth empirical analysis of the most progressive methods currently at our disposal. We also build a new benchmark dataset to facilitate a more robust evaluation and pinpoint enduring issues intrinsic to existing techniques. Our objective is to provide valuable insights into the effectiveness and feasibility of each editing technique, thereby assisting the community in making informed decisions on the selection of the most appropriate method for a specific task or context. Code and datasets are available at https://github.com/zjunlp/EasyEdit.
1 Introduction
Model editing aims to update or repair LLM behavior efficiently while preserving performance on unrelated inputs. This paper standardizes the problem and systematically compares existing methods, datasets, models, and editing settings.
- Model editing seeks to maintain LLM relevance and fix errors without retraining an entirely new model.
- Existing techniques either add an auxiliary network or modify parameters responsible for undesirable outputs.
- The literature lacked comprehensive comparisons under uniform experimental conditions, limiting understanding of methods’ relative strengths and weaknesses.
- Evaluation extends beyond factual editing to portability, locality, and efficiency, where current methods remain limited.
2 Problems Definition
Model editing changes a base model’s behavior for an edit example and its related inputs while preserving behavior elsewhere. The framework evaluates edited models through reliability, generalization, and locality.
- Model editing produces an edited model that changes behavior on a specified edit descriptor while avoiding effects on other samples.
- The editing scope includes the edit example and its equivalence neighborhood, whereas out-of-scope inputs are unrelated examples.
- A successful edit is assessed through reliability, generalization, and locality.
- Reliability: Reliability measures whether the post-edit model gives the target answer for the edited case.
- Generalization: Generalization measures whether the edit transfers to equivalent neighbors such as rephrased sentences.
- Locality: Locality measures whether predictions on irrelevant out-of-scope examples remain unchanged after editing.
3 Current Methods
LLM editing methods either preserve the original parameters through memory or additional components, or modify parameters directly. The paper distinguishes several parameter-editing strategies and compares their empirical behavior.
- Model editing methods follow two paradigms: modifying model parameters or preserving them.
- Methods for Preserving LLMs’ Parameters: Memory-based methods store edits and retrieve relevant facts to guide generation while retaining the original model.
- Methods for Preserving LLMs’ Parameters: Additional-parameter methods train new parameters on modified knowledge while keeping the original parameters static.
- Methods for Modifying LLMs’ Parameters: Locate-then-edit methods identify parameters associated with knowledge and update them directly; ROME uses causal mediation and constrained least squares.
- Methods for Modifying LLMs’ Parameters: MEMIT extends ROME to synchronous multi-case editing, while PMET additionally incorporates attention values.
- Methods for Modifying LLMs’ Parameters: Meta-learning methods use hypernetworks or gradient transformations to predict or learn parameter updates for editing language models.
4 Preliminary Experiments
The experiments compare model-editing methods across factual datasets, model scales, and editing regimes. Results show strong basic factual-editing performance for some methods, but scalability, locality, and sequential stability remain important constraints.
- Basic Model Results: SERAC and ROME achieve the strongest basic performance on ZsRE and COUNTERFACT, while MEMIT emphasizes reliability and locality despite weaker generalization.SERAC exceeds 90% on several metrics, whereas MEND exceeds 80% on T5 but trails ROME and SERAC.
- Batch Editing: Figure 3 plots batch-editing performance against batch number, testing MEMIT at [1,10,100,1000] and omitting batch 1000 for FT, SERAC, and MEND because of memory usage.The figure’s comparison is therefore most directly informative about performance as batch size grows under differing memory constraints.
- Model Scaling: ROME and MEMIT perform well on GPT-NEOX-20B but fail on OPT-13B because their matrix-inversion assumptions are not satisfied there.Least-squares approximation also produces unsatisfactory results, and MEMIT degrades more than ROME as models become larger.
- Batch Editing: MEMIT maintains robust reliability and generalization through 1000 batch edits, but locality decreases at that scale.FT-L, SERAC, and MEND were tested only through 100 edits because their memory requirements exceeded available capacity; SERAC remained perfect through 100 edits, while MEND and FT-L declined rapidly.
- Sequential Editing: Parameter-preserving methods such as SERAC and T-Patcher remain generally stable during sequential editing, whereas parameter-altering methods degrade as edits accumulate.ROME degrades after n = 10, MEMIT declines after 100 edits less sharply than ROME, and MEND declines significantly at n = 10.
5 Comprehensive Study
The study argues that standard metrics do not fully capture model-editing capabilities and evaluates portability, locality, and efficiency more comprehensively. Results show persistent weaknesses in robust generalization, distractor resistance, and editing cost.
- 5.1 Portability - Robust Generalization: Portability evaluates whether edited knowledge transfers to related content through subject replacement, reversed relations, and one-hop reasoning.The metric is computed as average accuracy on reasoning examples added to ZsRE.
- 5.1 Portability - Robust Generalization: Less than 20% accuracy across all portability aspects was achieved by SERAC, despite its strong performance on earlier metrics.SERAC’s bottleneck is attributed to classifier accuracy and the auxiliary model’s capabilities.
- 5.1 Portability - Robust Generalization: Over 90% accuracy on reversed relations was achieved by IKE on both GPT-J and GPT-NEOX-20B, while most methods edit only one-direction relations.FT-L, IKE, and T-patcher also perform strongly when subjects are substituted, whereas many methods fail to generalize the subject concept.
- 5.2 Locality - Side Effect of Model Editing: Locality results show strong preservation of other attributes but generally poor performance when edited cases distract the model.IKE remains relatively stable in distract-neighbor settings because it requires the edited fact to precede the input.
- 5.2 Locality - Side Effect of Model Editing: Parameter-preserving methods largely maintain commonsense-task performance, while parameter-altering methods generally reduce it, except MEMIT.MEMIT maintains strong commonsense performance despite changing parameters.
- 5.3 Efficiency: MEND requires more than 60GB for training, while MEND and SERAC also require substantial additional training before fast editing.MEND training on ZsRE took over 7 hours, and SERAC training took over 36 hours on 3× V100; T-Patcher is slowest during editing.
6 Relationship with Relevant Works
Model editing connects to continual learning, machine unlearning, and research on how pretrained language models store knowledge. It is also motivated by the need to remove harmful or private information from LLMs.
- Relationship with Relevant Works: Research on how pretrained language models store knowledge contributes principles for developing model-editing methods.This work includes studies of the mechanisms governing knowledge storage in language models.
- Relationship with Relevant Works: Model editing encompasses lifelong learning and unlearning, supporting adaptive addition, modification, and removal of knowledge.The paper relates these goals to continual learning and machine unlearning.
- Relationship with Relevant Works: Model editing may suppress harmful language generation and help address privacy and security concerns from unreliable or private model outputs.The paper frames erasing potentially harmful and private information as important for LLM-based applications.
7 Conclusion
The paper systematically analyzes LLM editing techniques and finds substantial room for improvement, especially in portability, locality, and efficiency. It aims to support better understanding and further research on these open issues.
- 7 Conclusion: The analysis examines existing editing techniques through their features, strengths, and limitations.Its stated purpose is to help researchers better understand current approaches.
- 7 Conclusion: Portability, locality, and efficiency remain the main areas requiring improvement in LLM editing.The paper presents these dimensions as persistent open issues for future research.
- 7 Conclusion: Improved LLM editing could help align models with changing user needs and values.The paper hopes its analysis will spur progress on these unresolved problems.
Limitations
The paper identifies limitations in model scale, architecture, editing scope, editing settings, knowledge erasure, and black-box model access.
- Model Scale & Architecture: Experiments cover models only up to 20B parameters, and effectiveness across architectures such as Llama remains unexplored.These boundaries arise from computational resource constraints and architectural differences.
- Editing Scope: Personality, emotions, opinions, beliefs, multilingual editing, and related non-factual applications are recognized but not detailed in the paper.These areas remain relatively uncharted or warrant future attention.
- Editing Setting: The comprehensive study mainly evaluates single edits, leaving multiple-edit evaluation for future work.A cited multi-hop reasoning setting explores generalization for multiple simultaneous edits, but this paper does not evaluate it.
- Editing Scope: The study focuses on changing outputs to reflect specific facts, while erasing knowledge stored in LLMs remains an important research direction.Knowledge injection and updating are distinguished from knowledge erasure.
- Editing Black-Box LLMs: Editing black-box LLMs such as ChatGPT and GPT-4 remains challenging because these models are accessible only through APIs.In-context learning and prompt-based methods have been explored as possible approaches.
Ethic Consideration
Model editing requires ethical safeguards because ill-intentioned edits could produce harmful or inappropriate outputs.
- Ethic Consideration: Ill-intentioned model editing could cause LLMs to generate harmful or inappropriate outputs, motivating safe and responsible practices.The paper calls for ethical considerations and safeguards against misuse.
A Implementing Details
The study implements and compares model-editing methods spanning multiple architectures, update paradigms, and editing scales.
- Architecture Compatibility: ROME and MEMIT are reported only for GPT-J because the empirical findings indicate they are suitable for decoder-only models.Results for T5-XL are not reported for these methods.
- Memory-Based Editing: SERAC uses an explicit edit memory, scope classifier, and counterfactual model to route in-scope inputs to edited predictions.Out-of-scope inputs retain the original model’s prediction.
- Locate-Then-Edit: Locate-then-edit methods identify knowledge-related parameters before modifying them; ROME uses rank-one MLP updates, while MEMIT extends editing to multiple cases.KN targets selected knowledge neurons, ROME modifies the MLP matrix, and MEMIT updates a range of critical layers.
- Additional Parameters: Additional-parameter methods freeze the original model and add trainable components, including patches or calibration neurons.T-Patcher adds one neuron per mistake, while CaliNET adds several neurons for multiple edit cases.
- T-Patcher: For autoregressive models, T-Patcher considers output-position errors and adds up to 5 patches for one edit example.The actual input concatenates the edit input and target output.
- In-Context Editing: IKE edits through in-context demonstrations without parameter modifications, making it applicable to existing LLMs.Its templates include copy, update, and retain formats.
B Dataset Details
The datasets evaluate factual editing through equivalence neighborhoods, portability, locality, and side effects using controlled constructions from ZsRE and COUNTERFACT.
- Basic Datasets: ZsRE uses back-translated question rephrasings as the equivalence neighborhood, while COUNTERFACT tests counterfactuals and locality with related subjects and Natural Questions.COUNTERFACT substitutes entities sharing a predicate to create out-of-scope examples.
- Portability: Portability data retain triples whose tail entity appears among the original model’s Top-10 link-prediction logits.GPT-4 then generates portability questions and answers from these selected triples.
- Portability: The final portability dataset intersects examples filtered by T5-XL and GPT-J to ensure both models possess prior knowledge of the triples.Reported link-prediction scores are 72.99 and 69.78 for GPT-J on ZsRE and COUNTERFACT, versus 83.90 and 84.81 for T5.
- Subject Replace: Subject-replace evaluation substitutes a subject with a Wikidata alias or a GPT-4-generated synonym to test generalization across subject representations.The construction uses Wikidata aliases when available and GPT-4 synonyms otherwise.
- Relation Replace: Relation-replace examples test whether an edited relation transfers to a new question while unrelated subject attributes remain unchanged.The dataset modifies 804 COUNTERFACT instances using randomly selected unrelated relations and GPT-4-generated questions.
- Portability: Figure 6 depicts the procedure for generating portability question-answer pairs with GPT-4.The figure presents dataset construction rather than model-performance results.
- Locality: Locality evaluation prepends an edited fact to an unrelated prompt to measure side effects from over-editing.The construction selects 804 data instances from COUNTERFACT.
B.3.3 Other Task
The study evaluates whether post-edited models preserve performance on an unrelated commonsense reasoning task, using PIQA as the downstream testbed. PIQA measures physics reasoning through two-way multiple-choice questions, with accuracy computed from the model’s selected answers.
- PIQA evaluates post-edited models’ performance on another downstream task to assess the impact of model editing techniques.The evaluation uses PIQA to reflect portability beyond the edited task.
- PIQA is a two-way multiple-choice question-answering task that tests commonsense physics reasoning about objects.
- The experiment evaluates continuously post-edited GPT-J models using 100 COUNTERFACT samples and measures PIQA accuracy.
- For each question, the model selects the option with the least perplexity, and correctness is recorded when that option matches the target answer.The indicator contributes to the accuracy calculation.
- Portability data are constructed from questions, subjects, altered answers, and manually created demonstrations, while an inverse-relation dataset uses questions and answers to generate inverse questions.