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arXiv · 2210.15452

Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity

Dennis Ulmer·Jes Frellsen·Christian Hardmeier
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Abstract

We investigate the problem of determining the predictive confidence (or, conversely, uncertainty) of a neural classifier through the lens of low-resource languages. By training models on sub-sampled datasets in three different languages, we assess the quality of estimates from a wide array of approaches and their dependence on the amount of available data. We find that while approaches based on pre-trained models and ensembles achieve the best results overall, the quality of uncertainty estimates can surprisingly suffer with more data. We also perform a qualitative analysis of uncertainties on sequences, discovering that a model's total uncertainty seems to be influenced to a large degree by its data uncertainty, not model uncertainty. All model implementations are open-sourced in a software package.

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Dennis Ulmer, Jes Frellsen, Christian Hardmeier. 2022-10-20. Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity. https://arxiv.org/abs/2210.15452

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