Neighboring Perturbations of Knowledge Editing on Large Language Models
Jun-Yu Ma Jia-Chen Gu Ningyu Zhang Zhen-Hua Ling
Despite their exceptional capabilities, large language models (LLMs) are
prone to generating unintended text due to false or outdated knowledge. Given
the resource-intensive nature of retraining LLMs, there has been a notable
increase in the development of knowledge editing. However, current approaches
and evaluations rarely explore the perturbation of editing on neighboring
knowledge. This paper studies whether updating new knowledge to LLMs perturbs
the neighboring knowledge encapsulated within them. Specifically, we seek to
figure out whether appending a new answer into an answer list to a factual
question leads to catastrophic forgetting of original correct answers in this
list, as well as unintentional inclusion of incorrect answers. A metric of
additivity is introduced and a benchmark dubbed as Perturbation Evaluation of
Appending Knowledge (PEAK) is constructed to evaluate the degree of
perturbation to neighboring knowledge when appending new knowledge. Besides, a
plug-and-play framework termed Appending via Preservation and Prevention (APP)
is proposed to mitigate the neighboring perturbation by maintaining the
integrity of the answer list. Experiments demonstrate the effectiveness of APP
coupling with four editing methods on three LLMs.