As the size of the pre-trained language model (PLM) continues to increase,
numerous parameter-efficient transfer learning methods have been proposed
recently to compensate for the tremendous cost of fine-tuning. Despite the
impressive results achieved by large pre-trained language models (PLMs) and
various parameter-efficient transfer learning (PETL) methods on sundry
benchmarks, it remains unclear if they can handle inputs that have been
distributionally shifted effectively. In this study, we systematically explore
how the ability to detect out-of-distribution (OOD) changes as the size of the
PLM grows or the transfer methods are altered. Specifically, we evaluated
various PETL techniques, including fine-tuning, Adapter, LoRA, and
prefix-tuning, on three different intention classification tasks, each
utilizing various language models with different scales.

本研究系统地探讨了在预训练语言模型（Pre-trained Language Models）规模扩大或转移方法改变时，检测样本分布变化的能力如何随之改变，着重评估了各种 PETL 技术在三个不同意图分类任务上的效果。