In this paper, we focus on quantifying model stability as a function of
random seed by investigating the effects of the induced randomness on model
performance and the robustness of the model in general. We specifically perform
a controlled study on the effect of random seeds on the behaviour of attention,
gradient-based and surrogate model based (LIME) interpretations. Our analysis
suggests that random seeds can adversely affect the consistency of models
resulting in counterfactual interpretations. We propose a technique called
Aggressive Stochastic Weight Averaging (ASWA)and an extension called
Norm-filtered Aggressive Stochastic Weight Averaging (NASWA) which improves the
stability of models over random seeds. With our ASWA and NASWA based
optimization, we are able to improve the robustness of the original model, on
average reducing the standard deviation of the model's performance by 72%.

本文旨在通过调查感知随机性对模型性能和鲁棒性的影响，将模型稳定性量化为随机种子的函数。我们特别研究了随机种子对注意力、基于梯度和替代模型的（LIME）解释行为的影响。我们提出了一种名为 ASWA（Aggressive Stochastic Weight Averaging）的技术及其扩展版本 NASWA（Norm-filtered Aggressive Stochastic Weight Averaging），以提高对种子的模型稳定性。通过我们基于 ASWA 和 NASWA 的优化，我们能够提高原始模型的鲁棒性，平均缩减模型性能的标准差 72％。