  Large language models (LLMs) have played a pivotal role in building
communicative AI to imitate human behaviors but face the challenge of efficient
customization. To tackle this challenge, recent studies have delved into the
realm of model editing, which manipulates specific memories of language models
and changes the related language generation. However, the robustness of model
editing remains an open question. This work seeks to understand the strengths
and limitations of editing methods, thus facilitating robust, realistic
applications of communicative AI. Concretely, we conduct extensive analysis to
address the three key research questions. Q1: Can edited LLMs behave
consistently resembling communicative AI in realistic situations? Q2: To what
extent does the rephrasing of prompts lead LLMs to deviate from the edited
knowledge memory? Q3: Which knowledge features are correlated with the
performance and robustness of editing? Our experimental results uncover a
substantial disparity between existing editing methods and the practical
application of LLMs. On rephrased prompts that are complex and flexible but
common in realistic applications, the performance of editing experiences a
significant decline. Further analysis shows that more popular knowledge is
memorized better, easier to recall, and more challenging to edit effectively.
