  The current trend toward ever-larger models makes standard retraining
procedures an ever-more expensive burden. For this reason, there is growing
interest in model editing, which enables computationally inexpensive,
interpretable, post-hoc model modifications. While many model editing
techniques are promising, research on the properties of edited models is
largely limited to evaluation of validation accuracy. The robustness of edited
models is an important and yet mostly unexplored topic. In this paper, we
employ recently developed techniques from the field of deep learning robustness
to investigate both how model editing affects the general robustness of a
model, as well as the robustness of the specific behavior targeted by the edit.
We find that edits tend to reduce general robustness, but that the degree of
degradation depends on the editing algorithm and layers chosen. Motivated by
these observations we introduce a new model editing algorithm, 1-layer
interpolation (1-LI), which uses weight-space interpolation to navigate the
trade-off between editing task accuracy and general robustness.
