Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis

Autor:	Xiao, Yuxin, Liang, Paul Pu, Bhatt, Umang, Neiswanger, Willie, Salakhutdinov, Ruslan, Morency, Louis-Philippe
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Pre-trained language models (PLMs) have gained increasing popularity due to their compelling prediction performance in diverse natural language processing (NLP) tasks. When formulating a PLM-based prediction pipeline for NLP tasks, it is also crucial for the pipeline to minimize the calibration error, especially in safety-critical applications. That is, the pipeline should reliably indicate when we can trust its predictions. In particular, there are various considerations behind the pipeline: (1) the choice and (2) the size of PLM, (3) the choice of uncertainty quantifier, (4) the choice of fine-tuning loss, and many more. Although prior work has looked into some of these considerations, they usually draw conclusions based on a limited scope of empirical studies. There still lacks a holistic analysis on how to compose a well-calibrated PLM-based prediction pipeline. To fill this void, we compare a wide range of popular options for each consideration based on three prevalent NLP classification tasks and the setting of domain shift. In response, we recommend the following: (1) use ELECTRA for PLM encoding, (2) use larger PLMs if possible, (3) use Temp Scaling as the uncertainty quantifier, and (4) use Focal Loss for fine-tuning. Comment: Accepted by EMNLP 2022 (Findings)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.04714 Zobrazit plný text záznamu View this record from Arxiv