Propagation and Pitfalls: Reasoning-based Assessment of Knowledge
  Editing through Counterfactual Tasks
Wenyue Hua Jiang Guo Mingwen Dong Henghui Zhu Patrick Ng Zhiguo Wang
Current approaches of knowledge editing struggle to effectively propagate
updates to interconnected facts. In this work, we delve into the barriers that
hinder the appropriate propagation of updated knowledge within these models for
accurate reasoning. To support our analysis, we introduce a novel
reasoning-based benchmark -- ReCoE (Reasoning-based Counterfactual Editing
dataset) -- which covers six common reasoning schemes in real world. We conduct
a thorough analysis of existing knowledge editing techniques, including input
augmentation, finetuning, and locate-and-edit. We found that all model editing
methods show notably low performance on this dataset, especially in certain
reasoning schemes. Our analysis over the chain-of-thought generation of edited
models further uncover key reasons behind the inadequacy of existing knowledge
editing methods from a reasoning standpoint, involving aspects on fact-wise
editing, fact recall ability, and coherence in generation. We will make our
benchmark publicly available.