Diagnosing Model Editing via Knowledge Spectrum

2025-09-22

AI Summary

Existing model editing methods often induce undesirable side effects—such as edit failure or performance degradation—by neglecting intrinsic knowledge properties. To address this, we propose the “Knowledge Lineage” framework, the first systematic characterization of editing difficulty along three dimensions: real-world popularity, model familiarity, and linguistic structure. Building upon this, we introduce a “Knowledge Diagnostic Framework” that dynamically modulates editing intensity based on quantified knowledge characteristics. Through empirical analysis and multi-dimensional evaluation—including faithfulness, generalization, and locality—we achieve predictable and optimized editing outcomes. Our approach significantly improves both success rate and stability for high-difficulty edits, reduces computational overhead, and demonstrates strong effectiveness and generalizability across diverse editing scenarios (e.g., fact updating, counterfactual reasoning, and multi-hop editing).

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Model editing, the process of efficiently modifying factual knowledge in pre-trained language models, is critical for maintaining their accuracy and relevance. However, existing editing methods often introduce unintended side effects, degrading model performance in unpredictable ways. While much research has focused on improving editing algorithms, the role of the target knowledge's intrinsic properties remains a significant, underexplored factor. This paper addresses this gap by first proposing the ``Knowledge Spectrum,'' a systematic framework for categorizing knowledge based on its real-world popularity, the model's pre-edit familiarity, and the linguistic structure of the eliciting question. Our empirical analysis reveals that these characteristics are strong predictors of editing success and stability. Informed by these findings, we introduce the ``Knowledge-Diagnostic Framework,'' an adaptive strategy that tailors editing intensity to the diagnosed difficulty of a knowledge item. We demonstrate that this framework significantly improves success rates for challenging edits while optimizing computational resources. Our work provides a more comprehensive understanding of the factors governing model editing.

Problem

• Model editing introduces unintended side effects degrading performance
• Target knowledge's intrinsic properties remain underexplored factor
• Need adaptive strategy to tailor editing intensity for optimization

Innovation

• Proposed Knowledge Spectrum categorization framework
• Introduced adaptive Knowledge-Diagnostic Framework strategy
• Tailored editing intensity based on difficulty diagnosis