Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning

Information

Title Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning

Authors

Hyunsoo Cho, Choonghyun Park, Jun Yeob Kim, Hyuhng Joon Kim, Kang Min Yoo, Sang-goo Lee

Year 2023 / 7

Keywords NLP, machine learning, language models, ICL, OOD

Publication Type International Conference

Publication The 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)

Link url

Abstract

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 high cost of fine-tuning. While large PLMs and various PETL methods have achieved impressive results on various benchmarks, it is uncertain whether they can effectively handle inputs that have been distributionally shifted. 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, with various language models with different scales.