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

• URL: http://arxiv.org/abs/2210.15452v1
• Date: Thu, 20 Oct 2022 15:42:02 GMT
• Title: Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity
• Authors: Dennis Ulmer, Jes Frellsen, Christian Hardmeier
• Abstract summary: 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.
• Score: 7.3372471678239215
• License: http://creativecommons.org/licenses/by/4.0/
• 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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