CollabEdit: Towards Non-destructive Collaborative Knowledge Editing
Jiamu Zheng Jinghuai Zhang Tianyu Du Xuhong Zhang Jianwei Yin Tao Lin
Collaborative learning of large language models (LLMs) has emerged as a
new paradigm for utilizing private data from different parties to guarantee
efﬁciency and privacy. Meanwhile, Knowledge Editing (KE) for LLMs has also
garnered increased attention due to its ability to manipulate the behaviors of
LLMs explicitly, yet leaves the collaborative KE case—in which knowledge
edits of multiple parties are aggregated in a privacy-preserving and continual
manner—unexamined. To this end, this manuscript dives into the ﬁrst investigation
 of collaborative KE, in which we start by carefully identifying the unique
three challenges therein, including knowledge overlap, knowledge conﬂict, and
knowledge forgetting. We then propose a non-destructive collaborative KE
framework, COLLABEDIT, which employs a novel model merging mechanism
to mimic the global KE behavior while preventing the severe performance drop.
Extensive experiments on two canonical datasets demonstrate the superiority of
COLLABEDIT compared to other destructive baselines, and results shed light on
addressing three collaborative KE challenges and future applications. Our code is
available at [https://github.com/LINs-lab/CollabEdit](https://github.com/LINs-lab/CollabEdit).
Collaborative Learning, Knowledge Editing