Abstract
We propose two novel test-time fine-tuning methods to improve uncertain model predictions. Our methods require no auxiliary data and use the given test instance only. Instead of performing a greedy selection of the most likely class to make a prediction, we introduce an additional focus on the likely classes step during inference. By applying a single-step gradient descent, we refine predictions when an initial forward pass indicates high uncertainty. This aligns predictions more closely with the ideal of assigning zero probability to less plausible outcomes. Our theoretical discussion provides a deeper understanding highlighting the impact on shared and non-shared features among (focus) classes. The experimental evaluation highlights accuracy gains on samples exhibiting high decision uncertainty for a diverse set of models from both the text and image domain using the same hyperparameters.
Abstract (translated)
我们提出了两种新颖的测试时间微调方法，旨在改进模型在不确定情况下的预测。这些方法不需要额外的数据支持，并且仅使用给定的测试实例。不同于执行贪婪选择最可能类别的策略以进行预测，我们在推理阶段引入了一个额外关注可能类别步骤的方法。通过应用单步梯度下降法，在初始前向传递表明高不确定性时，我们可以优化预测结果。这种方法使预测更加接近理想状态：即对不太可能的结果赋予零概率。 理论讨论深入探讨了共享和非共享特征在各类别中的影响，并提供了更深层次的理解。实验评估显示，使用相同的超参数设置，对于来自文本和图像领域多种模型中表现出高度决策不确定性的样本，我们的方法能够显著提高准确性。
URL
https://arxiv.org/abs/2505.03819
