Abstract
This study examines the role of uncertainty estimation (UE) methods in multilingual text classification under noisy and non-topical conditions. Using a complex-vs-simple sentence classification task across several languages, we evaluate a range of UE techniques against a range of metrics to assess their contribution to making more robust predictions. Results indicate that while methods relying on softmax outputs remain competitive in high-resource in-domain settings, their reliability declines in low-resource or domain-shift scenarios. In contrast, Monte Carlo dropout approaches demonstrate consistently strong performance across all languages, offering more robust calibration, stable decision thresholds, and greater discriminative power even under adverse conditions. We further demonstrate the positive impact of UE on non-topical classification: abstaining from predicting the 10\% most uncertain instances increases the macro F1 score from 0.81 to 0.85 in the Readme task. By integrating UE with trustworthiness metrics, this study provides actionable insights for developing more reliable NLP systems in real-world multilingual environments. See this https URL
Abstract (translated)
这项研究探讨了不确定性估计（UE）方法在多语言文本分类中噪声和非主题条件下的作用。通过跨几种语言的复杂句与简单句分类任务，我们评估了一系列不确定性估计技术，并使用一系列度量标准来评估它们对提高预测稳健性方面的贡献。结果显示，在高资源且领域内设置的情况下，依赖于softmax输出的方法仍然具有竞争力；但在低资源或领域迁移场景中，其可靠性会下降。相比之下，蒙特卡洛 dropout 方法在所有语言中的表现始终强劲，提供了更稳健的校准、稳定的决策阈值和更高的判别力，即使是在不利条件下也是如此。 我们还展示了 UE 对非主题分类的积极影响：放弃预测 10% 最不确定的实例后，在 Readme 任务中宏平均 F1 分数从 0.81 提升到了 0.85。通过将不确定性估计与可信度指标相结合，这项研究为在真实世界的多语言环境中开发更可靠的 NLP 系统提供了实用见解。 参见此 [链接](https://example.com)。
URL
https://arxiv.org/abs/2603.07330
