How Well Can Knowledge Edit Methods Edit Perplexing Knowledge?

TL;DR

This paper investigates how the conflicting nature of new knowledge, termed perplexingness, affects the ability of LLM editing methods to update their knowledge, revealing higher resistance to edits that violate learned hierarchies.

Contribution

The study introduces HierarchyData, a dataset for analyzing knowledge editing, and systematically examines how perplexingness impacts editing effectiveness across models and methods.

Findings

Higher perplexingness correlates with lower editing success.

Abstract concept edits are more resistant to modification.

Knowledge conflicts with learned hierarchies are harder to encode.

Abstract

Large language models (LLMs) have demonstrated remarkable capabilities, but updating their knowledge post-training remains a critical challenge. While recent model editing techniques like Rank-One Model Editing (ROME) show promise, their effectiveness may vary based on the nature of the knowledge being edited. We introduce the concept of ``perplexingness'': the degree to which new knowledge conflicts with an LLM's learned conceptual hierarchies and categorical relationships. For instance, editing ``British Shorthair is a kind of cat'' to ``British Shorthair is a kind of dog'' represents a low-perplexingness edit within the same taxonomic level, while editing ``A cat is a kind of animal'' to ``A cat is a kind of plant'' represents a high-perplexingness edit that violates fundamental categorical boundaries. To systematically investigate this phenomenon, we introduce HierarchyData, a carefully curated dataset of 99 hyponym-hypernym pairs across diverse categories. Through controlled experiments across three models and four editing methods, we demonstrate a strong negative correlation between the perplexingness of new knowledge and the effectiveness of knowledge editing. Our analysis reveals that edits involving more abstract concepts (hypernyms) generally exhibit higher perplexingness and are more resistant to modification than their specific counterparts (hyponyms). These findings highlight a fundamental challenge in LLM knowledge editing: the more a new fact contradicts an LLM's learned conceptual hierarchies, the harder it becomes to reliably encode that knowledge.