  Catastrophic Forgetting (CF) means models forgetting previously acquired
knowledge when learning new data. It compromises the effectiveness of large
language models (LLMs) during fine-tuning, yet the underlying causes have not
been thoroughly investigated. This paper takes the first step to reveal the
direct link between the flatness of the model loss landscape and the extent of
CF in the field of LLMs. Based on this, we introduce the sharpness-aware
minimization to mitigate CF by flattening the loss landscape. Experiments on
three widely-used fine-tuning datasets, spanning different model scales,
demonstrate the effectiveness of our method in alleviating CF. Analyses show
that we nicely complement the existing anti-forgetting strategies, further
enhancing the resistance of LLMs to CF.
