  As the typical retraining paradigm is unacceptably time- and
resource-consuming, researchers are turning to model editing in order to seek
an effective, consecutive, and batch-supportive way to edit the model behavior
directly. Despite all these practical expectations, existing model editing
methods fail to realize all of them. Furthermore, the memory demands for such
succession-supportive model editing approaches tend to be prohibitive,
frequently necessitating an external memory that grows incrementally over time.
To cope with these challenges, we propose COMEBA-HK, a model editing method
that is both consecutive and batch-supportive. COMEBA-HK is memory-friendly as
it only needs a small amount of it to store several hook layers with updated
weights. Experimental results demonstrate the superiority of our method over
other batch-supportive model editing methods under both single-round and
consecutive batch editing scenarios. Extensive analyses of COMEBA-HK have been
conducted to verify the stability of our method over 1) the number of
consecutive steps and 2) the number of editing instance.
