Despite significant progress in model editing methods, their application in
real-world scenarios remains challenging as they often cause large language
models (LLMs) to collapse. Among them, ROME is particularly concerning, as it
could disrupt LLMs with only a single edit. In this paper, we study the root
causes of such collapse. Through extensive analysis, we identify two primary
factors that contribute to the collapse: i) inconsistent handling of prefixed
and unprefixed keys in the parameter update equation may result in very small
denominators, causing excessively large parameter updates; ii) the subject of
collapse cases is usually the first token, whose unprefixed key distribution
significantly differs from the prefixed key distribution in autoregressive
transformers, causing the aforementioned issue to materialize. To validate our
analysis, we propose a simple yet effective approach: uniformly using prefixed
keys during editing phase and adding prefixes during the testing phase. The
experimental results show that the proposed solution can prevent model collapse
while maintaining the effectiveness of the edits.

尽管模型编辑方法取得了显著进展，但在实际场景中应用仍然具有挑战性，因为它们经常导致大型语言模型发生崩溃。本文研究了这种崩溃的根本原因，并通过广泛的分析，确定了导致崩溃的两个主要因素。为了验证我们的分析，我们提出了一种简单而有效的方法：在编辑阶段统一使用带前缀的键，并在测试阶段添加前缀。实验结果表明，这种解决方案可以预防模型崩溃，同时保持编辑的有效性。