  Although model editing has shown promise in revising knowledge in Large
Language Models (LLMs), its impact on the inherent capabilities of LLMs is
often overlooked. In this work, we reveal a critical phenomenon: even a single
edit can trigger model collapse, manifesting as significant performance
degradation in various benchmark tasks. However, benchmarking LLMs after each
edit, while necessary to prevent such collapses, is impractically
time-consuming and resource-intensive. To mitigate this, we propose using
perplexity as a surrogate metric, validated by extensive experiments
demonstrating changes in an edited model's perplexity are strongly correlated
with its downstream task performances. We further conduct an in-depth study on
sequential editing, a practical setting for real-world scenarios, across
various editing methods and LLMs, focusing on hard cases from our previous
single edit studies. The results indicate that nearly all examined editing
methods result in model collapse after only few edits. To facilitate further
research, we have utilized GPT-3.5 to develop a new dataset, HardEdit, based on
those hard cases. This dataset aims to establish the foundation for pioneering
research in reliable model editing and the mechanisms underlying
editing-induced model collapse. We hope this work can draw the community's
attention to the potential risks inherent in model editing practices.
