Does correcting an LLM's knowledge break other capabilities?

Knowledge editing is a method for updating specific relationships within the model. While producing a new answer for the target prompt may appear to be editing success, it may not be reflected in questions with altered wording or related reasoning.

In editing evaluation, we separate the original question, paraphrases, different formats, and related one-step reasoning. If the answer changes only due to literal triggers, it is fragile as a practical update.

We also pose counter-directional questions. We verify not only whether new facts can be answered but also whether reasoning based on old facts can be rejected or corrected. Outputting contradictions between new and old facts simultaneously results in failure.

We save the justification for the correct answer and its effective time point. If we cannot track which edit should overwrite when facts change again, only the update history will accumulate within the parameters.

We do not treat post-editing explanations as proof of internal causality. Since we cannot determine how the model memorized the new fact solely from output explanations, we focus on behavioral tests.

• Target success: Correctly answering the updated question
• Paraphrase: The update is reflected even when the expression is changed
• Related reasoning: Reasoning requiring the new fact is consistent
• Old knowledge suppression: Not reproducing old facts unconditionally

Locality means that outputs other than the change target do not change more than necessary. We test not only completely unrelated general questions but also proximate knowledge such as other attributes of the same subject and other subjects of the same relationship.

If editing is too broad, it overwrites correct facts similar to the target. If too narrow, it is not reflected in paraphrases. Since generalization and locality are in tension, we do not maximize a single score.

Gu et al. reported that knowledge editing can harm downstream general capabilities across multiple editing methods and models, and explored mitigation via regularization. [2] Relying solely on target editing success overlooks side effects.

The locality set intentionally includes a large amount of peripheral knowledge to the editing target. If only random general questions are used, narrow-range destruction is buried in the average. We prioritize concepts referenced together in business operations.

We do not count all examples where the output changed as failures. There are related answers that should logically change due to the new fact. We distinguish expected spillover from unnecessary spillover in advance using a causal relationship table.

Scores for tasks that appear unrelated to the editing target, such as summarization, question answering, natural language inference, and sentiment analysis, may decline. [2] General capabilities are not an auxiliary indicator for editing success but another adoption lower bound.

We look at the worst-case difference in critical capabilities, not just average scores. In areas where even slight damage to safety rejection or instruction following has a large impact, we do not allow compensation through improvements in other tasks.

The regression set is created from the model's original strengths and actual usage functions. It includes not only general benchmarks but also enterprise-specific long texts, languages, and output formats.

For examples where the output changed before and after editing, we classify the reasons in addition to correctness. We distinguish between legitimate changes accompanying knowledge updates, format fluctuations, reasoning degradation, and changes in safety constraints.

To avoid confusing the model's stochastic fluctuations with editing effects, we repeat with the same settings and compare with an unedited control model. We verify whether the difference exceeds natural variation.

• Usage capabilities: Re-test summarization, extraction, and reasoning used in production
• Safety capabilities: Observe regression in rejection, confidentiality, and prohibited behaviors
• Format capabilities: Check for collapse in structured output and citations
• Worst-case difference: Do not average out the decline in critical capabilities

In actual operations, editing does not end with a single instance. Gupta et al. showed that increasing the number of edits can lead to gradual forgetting and sudden capability collapse. [1] We cannot extrapolate single-shot benchmark success rates to continuous operations.

After each edit, we re-test not only the latest target but also past edits, original knowledge, and general capabilities. New corrections may erase old ones or revive old misinformation.

We also try changing the editing order. Interference may differ when correcting semantically close facts consecutively versus correcting distant facts alternately. We reflect the order common in operations into the evaluation set.

We link cumulative changes to the model version and track from which edit the lower bound was breached. If there is no edit log and only the final state remains, identifying causes and rolling back becomes difficult.

If degradation exceeds the threshold, do not attempt to repair by further editing; instead, switch to the pre-edit model, retraining, or RAG. Define stop conditions in advance.

Methods that constrain parameter changes due to editing may improve the maintenance of general capabilities. Xu et al. proposed Editing Anchor Compression and examined the maintenance of capabilities during continuous editing across multiple models and tasks. [3]

We do not take reported values of mitigation methods as safety guarantees as is. Effects vary if the target model, editing method, task, and number of edits differ. [3] We re-evaluate using our own editing distribution and general capability set.

Even if capability maintenance improves, the edited knowledge may not settle sufficiently. We compare side effects and editing success under the same conditions and do not adopt improvement in only one as a reason for adoption.

Regularization and anchor selection themselves increase new settings and implementation. We include reproducibility, computational cost, model version dependency, and rollback upon failure in operational costs.

We examine knowledge types where failures concentrate even after mitigation. We distinguish proper nouns, time-dependent facts, multiple relationships, numbers, and negations, and exclude types where editing is ineffective.

If latest information can be referenced from external documents, managing justification and time points via RAG without editing parameters may be more auditable in some cases. We compare the frequency of fact updates and source requirements.

Knowledge editing may reduce search costs at query time but may lose update visibility and ease of revocation. We include not only operational costs but also change tracking and recovery costs.

For systematic updates of numerous facts, fine-tuning or retraining may be more appropriate than accumulating individual edits. We determine the switching point based on the number of targets, interdependencies, assurance levels, and computational costs.

Prices, positions, regulations, and inventory that change frequently are prone to obsolescence if fixed within parameters. If sources and effective time points are required in answers, we prioritize external knowledge.

We conduct method comparisons using the same question set, aligning target correctness, citability, general capabilities, update time, and rollback. We do not choose editing solely based on novelty.

Each edit is assigned a reason, justification, requester, approver, target relationship, expected spillover, prohibited spillover, and deadline. Without tracking who decided what is correct, technical success cannot be governed.

After editing, we do not automatically expand the impact scope to production; instead, we verify target, locality, and general capabilities in a staging environment. We approve only qualified edits collectively as a new model version.

We verify whether rollback can revert just the last edit or the entire model version. If edits are interdependent, partial rollback may create other contradictions.

In production, we monitor incorrect answers regarding the editing target, revival of old facts, anomalies in related knowledge, and regression of general capabilities. We track not only immediately after updates but also after subsequent edits and model changes.

Knowledge editing is neither a magic eraser nor an overwrite ledger. It is a change technology that can only be used when limited changes are falsifiable, spillover and cumulative degradation are measured, and one can revert to alternative methods.

The change record separates edit ID, target fact, model version, and restoration point from acceptance criteria: minima for locality and retained capability and maxima for spillover and cumulative degradation. A failed edit is not accumulated into later versions; the system returns to the restoration point. The editing path restarts only after a different evaluator passes regression including unedited domains and the change board approves those test results after the cause is corrected.