Related Knowledge Perturbation Matters: Rethinking Multiple Pieces of Knowledge Editing in Same-Subject

2025-02-08

AI Summary

This work addresses the degradation in multi-attribute editing performance for the same subject in knowledge editing, identifying “related-knowledge perturbation”—where subsequent edits undermine prior ones due to excessive reliance on subject representations. To formalize and validate this phenomenon’s prevalence, we introduce the Same-Subject Related Knowledge Editing (S²RKE) benchmark—the first of its kind. Building upon localization-editing paradigms (e.g., ROME, MEMIT), we propose a causal tracing and modular attribution framework that quantifies the negative correlation between subject representation dependency and edit consistency. Empirical results demonstrate that reducing subject representation dependency significantly improves multi-step editing consistency. Our contributions are twofold: (1) establishing S²RKE as a novel evaluation standard for intra-subject knowledge consistency, and (2) proposing “weakening strong subject-binding” as a fundamental design principle for robust knowledge editing.

Technology Category

• Knowledge Representation and Reasoning: Action, Change, and Causality
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Knowledge editing has become a promising approach for efficiently and precisely updating knowledge embedded in large language models (LLMs). In this work, we focus on Same-Subject Editing, which involves modifying multiple attributes of a single entity to ensure comprehensive and consistent updates to entity-centric knowledge. Through preliminary observation, we identify a significant challenge: Current state-of-the-art editing methods struggle when tasked with editing multiple related knowledge pieces for the same subject. To address the lack of relevant editing data for identical subjects in traditional benchmarks, we introduce the $ ext{S}^2 ext{RKE}$(Same-Subject Related Knowledge Editing) benchmark. Our extensive experiments reveal that only mainstream locate-then-edit methods, such as ROME and MEMIT, exhibit"related knowledge perturbation,"where subsequent edits interfere with earlier ones. Further analysis reveals that these methods over-rely on subject information, neglecting other critical factors, resulting in reduced editing effectiveness.

Problem

• Challenges in editing multiple related knowledge pieces
• Lack of relevant editing data for identical subjects
• Over-reliance on subject information reduces effectiveness

Innovation

• Same-Subject Editing technique
• Introducing S2RKE benchmark
• Analyzing related knowledge perturbation