  Large Language Models have revolutionized numerous tasks with their
remarkable efficacy. However, editing these models, crucial for rectifying
outdated or erroneous information, often leads to a complex issue known as the
ripple effect in the hidden space. While difficult to detect, this effect can
significantly impede the efficacy of model editing tasks and deteriorate model
performance. This paper addresses this scientific challenge by proposing a
novel evaluation methodology, Graphical Impact Evaluation(GIE), which
quantitatively evaluates the adaptations of the model and the subsequent impact
of editing. Furthermore, we introduce the Selective Impact Revision(SIR), a
model editing method designed to mitigate this ripple effect. Our comprehensive
evaluations reveal that the ripple effect in the hidden space is a significant
issue in all current model editing methods. However, our proposed methods, GIE
and SIR, effectively identify and alleviate this issue, contributing to the
advancement of LLM editing techniques.
