Unveiling the Pitfalls of Knowledge Editing for Large Language Models

TL;DR

This work interrogates the unintended consequences of knowledge editing in large language models by formalizing two pitfall types—Knowledge Conflict and Knowledge Distortion—and constructing dedicated benchmarks (ConflictEdit, RoundEdit) to quantify them. It analyzes multiple editing methods (FT, MEND, ROME, MEMIT) and reveals that accumulating edits can cause conflicts and irreversible distortions in the model's implicit knowledge structure. The authors propose metrics and evaluation protocols to diagnose these effects and introduce a practical mitigation called Multi-Label Edit (MLE), which edits multiple related labels to preserve knowledge coherence. Overall, the paper highlights the need for conflict-aware and distortion-aware evaluation in knowledge editing and points to future work integrating logical rules and KG reasoning for safer updates.

Abstract

As the cost associated with fine-tuning Large Language Models (LLMs) continues to rise, recent research efforts have pivoted towards developing methodologies to edit implicit knowledge embedded within LLMs. Yet, there's still a dark cloud lingering overhead -- will knowledge editing trigger butterfly effect? since it is still unclear whether knowledge editing might introduce side effects that pose potential risks or not. This paper pioneers the investigation into the potential pitfalls associated with knowledge editing for LLMs. To achieve this, we introduce new benchmark datasets and propose innovative evaluation metrics. Our results underline two pivotal concerns: (1) Knowledge Conflict: Editing groups of facts that logically clash can magnify the inherent inconsistencies in LLMs-a facet neglected by previous methods. (2) Knowledge Distortion: Altering parameters with the aim of editing factual knowledge can irrevocably warp the innate knowledge structure of LLMs. Experimental results vividly demonstrate that knowledge editing might inadvertently cast a shadow of unintended consequences on LLMs, which warrant attention and efforts for future works. Code and data are available at https://github.com/zjunlp/PitfallsKnowledgeEditing.

Figures (7)

• Figure 1: As the number of edits increases, the model might manifest Knowledge Conflict when dealing with inputs involved with multiple consecutive edits. Meanwhile, each edit could potentially lead to ruptures in knowledge links within the model, resulting in Knowledge Distortion.
• Figure 2: Unveiling the pitfalls of knowledge editing for LLMs. (a) Through Reverse Edit and Composite Edit, we can observe that previous knowledge editing approaches may trigger Knowledge Conflict, leading to failures of knowledge editing; (b) Through Round-Edit, we notice that previous knowledge editing approaches may lead to Knowledge Distortion, and the underlying knowledge structure within LLMs can be disrupted.
• Figure 3: A Unified View of Knowledge Conflict. $e_1$ and $e_2$ are two different knowledge editing instances. Editing Scope is the range where an edit takes effect, and Target is the expected editing object. (a) Coverage Edit shares a total coverage in editing scope. (b) Reverse Edit relates each edit through reverse facts. (c) Composite Edit is unique in its editing scope yet maintains a consistent logical rule concerning a tied fact denoted as $k_f$.
• Figure 4: The Easy and Hard split of RoundEdit. (a) The Easy split contains editing targets which are semantically associated with the true labels of $(s, r)$. The related field is called Migration Range. (b) The Hard split edits the object that is irrelevant to the true labels.
• Figure : Input: James J. Lovelace was educated at _. (a) Easy