Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue

1 Introduction

• Model editing provides targeted, resource-efficient knowledge updates without retraining, addressing the need to incorporate changing information into LLMs.The paper frames this against hallucinations associated with missing, false, or outdated embedded knowledge.Source: §1 · p. 1
• Across four editing methods, three LLMs, and eight tasks, editing can impair general abilities and obstruct simultaneous factuality gains.With a single pass changing less than 1% of parameters, LLaMA1 (7B) fell to nearly 0 on all tried tasks.Source: §1 · p. 2
• Excessive changes to original weights overfit edited facts, and accumulated overfitting can amplify harm to general abilities.Edited models may recall new facts well yet fail to generalize across downstream tasks.Source: §1 · p. 2
• RECT regularizes edit-update complexity to mitigate side effects while retaining over 94% editing performance.It keeps the top-k% elements with the greatest relative weight change and sets the remaining update elements to zero.Source: §1 · p. 2

2 Related Work

• Prior model-editing research spans memory-based, meta-learning, and locate-then-edit approaches.Memory-based approaches store edits externally, while meta-learning and locate-then-edit methods directly modify model parameters.Source: §2 · pp. 2–3
• Meta-learning uses a hypernetwork to generate parameter updates, whereas locate-then-edit methods identify and modify neurons or MLPs linked to stored knowledge (Cao et al., 2021; Mitchell et al., 2022a; Dai et al., 2022; Meng et al., 2022, 2023).Related work also studies editing evaluation and applications in areas such as personality, multimodal models, and privacy.Source: §2 · pp. 2–3
• Unlike work focused on editing algorithms or evaluation paradigms, this study evaluates side effects across tasks and proposes a regularizer; contemporaneous work reports catastrophic forgetting at scale but no mitigation.The authors describe their study as a systematic evaluation of four editing methods on three LLMs across eight tasks, coupled with analysis of causes.Source: §2 · p. 3

3 Preliminary

• Model editing changes memorized facts in language models without retraining to suit particular tasks or requirements.The paper notes that edits may target complex beliefs such as logical, spatial, or numerical knowledge.Source: §3 · p. 3
• The paper represents factual knowledge as a subject-relation-object triple and an edit as replacing its object with a new one.Given editing facts E and model f, a function K yields the edited model f*: K(f, E) = f*.Source: §3 · p. 3
• Editing effectiveness is assessed through reliability of recalling edited facts, generalization to paraphrase prompts, and locality on unrelated inputs (Cao et al., 2021; Mitchell et al., 2022a; Meng et al., 2022, 2023).Locality checks whether outputs for unrelated inputs remain consistent after editing.Source: §3 · p. 3

4.1 Evaluation Paradigm

• The evaluation contrasts single with sequential editing and instance with batch editing, measuring zero-shot performance on tasks unrelated to edited facts.Batch editing considers updating hundreds or thousands of facts simultaneously, while instance editing uses one instance per operation.Source: §4.1 · pp. 3–4

4.2 Evaluation Setup

• Experiments covered four editing methods, three LLMs, and eight general-ability tasks, using ZSRE edits and zero-shot downstream evaluation.KN and ROME support instance editing; MEND and MEMIT also support batch editing.Source: §4.2 · pp. 4–5

4.3 Results

• KN and ROME edits caused fluctuating, downward-trending general-task performance as edit counts rose; KN produced near-zero scores across selected tasks after one edit.The authors report this despite editing less than 1% of parameters.Source: §4.3 · p. 5, Figure 3 · p. 5
• MEND and MEMIT performance generally declined as batch size increased, and joint batch-sequential results echoed the separate-setting trends.The batch-size decline appeared in most cases, even with a single editing operation.Source: §4.3 · p. 6, Figure 4 · p. 6

4.4 Analysis of Causes of Side Effects

• Editing changes original weights excessively and overfits to edited facts; accumulated edits amplify weight changes and the share of elements above relative-change thresholds.Across 100 random single ZSRE edits, 20% of update elements had δ > 0.077 and 10% had δ > 0.171; visualizations also show accumulation amplifying changes.Source: §4.4 · pp. 6–7, Figure 5 · p. 7

5.1 Approach

• RECT uses relative weight change to retain the top-k% update elements and zero the remainder, constraining update complexity to curb overfitting.The method treats the largest-change elements as principal editing information and the rest as minor contributions.Source: §5.1 · pp. 7–8

5.3 Results of RECT

• RECT top-40% maintains over 94% majority of reliability and generalization, improves locality, and outperforms Random 40% and PCA 40%.The authors report that setting more elements to zero, such as RECT top-20%, can hurt editing performance by removing important editing information.Source: Figure 7 · p. 8, §5.3 · p. 8
• Regularization preserves abilities versus unregularized models on most tasks, including summarization and QA, but sentiment analysis remains challenging and larger edit counts are unclear.The reported comparisons include GPT-2 XL, with further downstream-task results in Appendix D.2.Source: Figure 8 · p. 8, §5.3 · p. 9

5.2 Regularization Baselines

• The baselines are unregularized updates, random selection of k% of update elements, and PCA compression of the most important editing information into k%.Random k% and PCA k% set the remaining update elements to zero.Source: §5.2 · p. 8

• Across instance/batch and single/sequential editing, current methods hurt general abilities; RECT is reported to prevent overfitting while preserving editing and downstream performance.Source: §6 · p. 9

• Because factuality gains may trade off with general abilities, the paper calls for considering long-term general performance alongside immediate factuality.Source: Impact Statements · p. 9

Limitations

• The side-effect study uses the ZSRE editing dataset, so more editing datasets are needed to explore hypothesized complex and diverse effects.The authors also leave theoretical analysis of edited models’ general-ability bottlenecks for future work.Source: Limitations · p. 9
• Whether mitigation remains effective for larger numbers of edits is unresolved because the paper does not test that setting.The authors also identify comparing editing methods at matched editing-performance and general-ability requirements as future work.Source: Limitations · p. 9

A Details of Evaluation Metrics

• Reliability measures whether the edited model assigns a higher probability to the updated fact than the original prediction.The example changes the predicted U.S. president from Donald Trump to Joe Biden.Source: A Details of Evaluation Metrics · p. 13
• Generalization measures whether the model recalls edited knowledge under paraphrased prompts within the editing scope.The example tests alternate phrasings asking who currently holds the U.S. presidency.Source: A Details of Evaluation Metrics · p. 13
• Locality measures whether responses to prompts irrelevant to the edit remain unchanged.The example expects the answer about France’s president to remain Emmanuel Macron.Source: A Details of Evaluation Metrics · p. 13

B Task Prompts

• Table 2 lists the prompts used to evaluate zero-shot performance on the general tasks.Source: Table 2 · p. 13, B Task Prompts · p. 13

C Editing Methods & Datasets

• Table 3 compares editing methods by training requirements, batch-edit capability, and parameters updated.It defines L as the number of layers, MLP as FFN, MLPproj as the second FFN linear layer, and neuron as the FFN key-value pair.Source: Table 3 · p. 14
• KN selects knowledge-associated neurons using gradient-based attributions and modifies corresponding MLP rows with scaled embedding vectors.KN 2.Source: C Editing Methods & Datasets · p. 14
• MEND uses a hypernetwork to produce weight updates by decomposing fine-tuning gradients into rank-1 form.MEND (Mitchell et al., 2022a)3.Source: C Editing Methods & Datasets · p. 14
• ROME localizes factual knowledge in a transformer MLP layer and updates it by writing new key-value pairs.ROME 4.Source: C Editing Methods & Datasets · p. 14
• MEMIT extends ROME to edit larger amounts of factual data by updating a sequence of MLP layers.MEMIT 5.Source: C Editing Methods & Datasets · p. 14
• The experiments use ZSRE, a question-answering dataset with back-translation paraphrases and alternative labels sampled from a BART-base model.Editing instances were randomly sampled, and experiments used the EasyEdit tool for standardized, reproducible evaluation.Source: C Editing Methods & Datasets · p. 14

D.1 Results of Side Effects of Model Editing

• Figures 9–11 compare general-task performance for KN or ROME on LLaMA-2, MEND or MEMIT across LLaMA-2 batch sizes, and MEND or MEMIT on GPT2-XL as edit counts increase.The captions distinguish instance versus sequential editing in Figure 9, batch versus single editing in Figure 10, and batch versus sequential editing in Figure 11.Source: Figure 9 · p. 15, Figure 10 · p. 15, Figure 11 · p. 16
• Figures 12–16 track general-task performance as edit counts increase for MEND or MEMIT on GPT2-XL, LLaMA-1 (7B), and LLaMA-2 (7B).The captions distinguish batch from sequential editing.Source: Figure 12 · p. 16, Figure 13 · p. 17, Figure 14 · p. 17

D.2 Results of RECT

• Figures 17 and 18 compare regularization methods and examine editing or downstream-task performance across RECT top-k% settings.Figure 18 specifies ROME and GPT-2 XL; the captions do not state which method or setting performs best.Source: Figure 17 · p. 19, Figure 18 · p. 19