Multilingual pre-trained language models are incredibly effective at Question
Answering (QA), a core task in Natural Language Understanding, achieving high
accuracies on several multilingual benchmarks. However, little is known about
how well they are calibrated. In this paper, we study the calibration
properties of several pre-trained multilingual large language models (LLMs) on
a variety of question-answering tasks. We perform extensive experiments,
spanning both extractive and generative QA model designs and diverse languages,
spanning both high-resource and low-resource ones. We study different
dimensions of calibration in in-distribution, out-of-distribution, and
cross-lingual transfer settings, and investigate strategies to improve it,
including post-hoc methods and regularized fine-tuning. We demonstrate
automatically translated data augmentation as a highly effective technique to
improve model calibration. We also conduct a number of ablation experiments to
study the effect of model size on calibration and how multilingual models
compare with their monolingual counterparts for diverse tasks and languages.

这篇论文研究了多语言预训练语言模型在问答任务中的校准性质，包括从不同维度研究了其在分布内、分布外和跨语言迁移设置下的校准情况，以及改进校准性的策略和技术。通过实验证明了自动翻译数据增强是提高模型校准性的一种高效技术，并进行了模型大小和多语言模型在不同任务和语言下与单语模型的比较的实验。