  Model editing is a technique that edits the large language models (LLMs) with
updated knowledge to alleviate hallucinations without resource-intensive
retraining. While current model editing methods can effectively modify a
model's behavior within a specific area of interest, they often overlook the
potential unintended side effects on the general abilities of LLMs such as
reasoning, natural language inference, and question answering. In this paper,
we raise concerns that model editing's improvements on factuality may come at
the cost of a significant degradation of the model's general abilities. We
systematically analyze the side effects by evaluating four popular editing
methods on three LLMs across eight representative tasks. Our extensive
empirical experiments show that it is challenging for current editing methods
to simultaneously improve factuality of LLMs and maintain their general
abilities. Our analysis reveals that the side effects are caused by model
editing altering the original model weights excessively, leading to overfitting
to the edited facts. To mitigate this, a method named RECT (RElative Change in
weighT) is proposed to regularize the edit update weights. Evaluation results
show that RECT can significantly mitigate the side effects of editing while
still maintaining over 94% editing performance.
