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.

通过开发内存友好型的 COMEBA-HK 模型编辑方法，我们解决了连续批量支持型模型编辑方法在时间和资源消耗方面的挑战，并在单轮和连续批量编辑场景下证明了我们方法的卓越性能。