通过知识谱系诊断模型编辑难易度，提升准确性与效率
Diagnosing Model Editing via Knowledge Spectrum
- 构建知识谱系框架，从流行度、模型熟悉度和提问结构分类知识
- 实证发现知识特性可预测编辑成功率与稳定性
- 提出自适应编辑强度策略，适合高难度知识修正场景
模型编辑旨在高效修改预训练语言模型中的事实知识，对维持其准确性和相关性至关重要。然而现有方法常引入不可预测的副作用，损害模型性能。尽管研究多聚焦于改进编辑算法，目标知识的内在属性仍被严重忽视。本文首次提出“知识谱系”框架，基于现实流行度、模型预编辑熟悉度及提问的语言结构对知识进行系统分类。实证分析表明，这些特征是编辑成功与稳定性的强预测因子。据此，我们提出“知识诊断框架”，根据知识项的诊断难度动态调整编辑强度。实验表明该框架显著提升复杂编辑的成功率，同时优化计算资源使用。本研究深化了对模型编辑决定因素的理解。
原文摘要 · Abstract (English)
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.
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