Language models (LMs) are known to suffer from forgetting of previously learned examples when fine-tuned, breaking stability of deployed LM systems. Despite efforts on mitigating forgetting, few have investigated whether, and how forgotten upstream examples are associated with newly learned tasks. Insights on such associations enable efficient and targeted mitigation of forgetting. In this paper, we empirically analyze forgetting that occurs in $N$ upstream examples while the model learns $M$ new tasks and visualize their associations with a $M \times N$ matrix. We empirically demonstrate that the degree of forgetting can often be approximated by simple multiplicative contributions of the upstream examples and newly learned tasks. We also reveal more complicated patterns where specific subsets of examples are forgotten with statistics and visualization. Following our analysis, we predict forgetting that happens on upstream examples when learning a new task with matrix completion over the empirical associations, outperforming prior approaches that rely on trainable LMs. Project website: https://inklab.usc.edu/lm-forgetting-prediction/

本文通过对语言模型进行经验分析，发现忘记常常可以通过上游示例和新学习任务的简单乘法关系来近似，并揭示了特定子集示例的复杂忘记模式，在基于经验关联的矩阵补全方法中预测了在学习新任务时发生在上游示例上的遗忘，优于依赖可训练语言模型的先前方法。

深入剖析语言模型微调中的遗忘现象：基于示例关联的统计分析