As large language models (LLMs) are widely applied across various fields, model compression has become increasingly crucial for reducing costs and improving inference efficiency. Post-training pruning is a promising method that does not require resource-intensive iterative training and only needs a small amount of calibration data to assess the importance of parameters. Previous research has primarily focused on designing advanced pruning methods, while different calibration data's impact on pruning performance still lacks systematical exploration. We fill this blank and surprisingly observe that the effects of calibration data even value more than designing advanced pruning strategies, especially for high sparsity. Our preliminary exploration also discloses that using calibration data similar to the training data can yield better performance. As pre-training data is usually inaccessible for advanced LLMs, we further provide a self-generating calibration data synthesis strategy to construct feasible calibration data. We conduct experiments on the recent strong open-source LLMs (e.g., DCLM, and LLaMA-3), and the results show that the proposed method outperforms commonly used calibration data and can effectively enhance strong pruning methods (e.g., Wanda, OWL).

本研究针对大型语言模型剪枝中校准数据对性能的影响进行系统性探索，填补了该领域的空白。我们发现，校准数据的质量对剪枝效果的重要性超过了先进剪枝策略的设计，尤其在高稀疏率下尤为明显。研究提出的自生成校准数据合成策略为构建有效校准数据提供了新的方法，并显著提升了剪枝效果。

注意校准数据对大型语言模型剪枝的影响