@inproceedings{cho-etal-2023-probing,
    title = "Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning",
    author = "Cho, Hyunsoo  and
      Park, Choonghyun  and
      Kim, Junyeob  and
      Kim, Hyuhng Joon  and
      Yoo, Kang Min  and
      Lee, Sang-goo",
    editor = "Palmer, Alexis  and
      Camacho-collados, Jose",
    booktitle = "Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.starsem-1.21",
    doi = "10.18653/v1/2023.starsem-1.21",
    pages = "225--235",
    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.",
}

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.
Cho, Hyunsoo Park, Choonghyun Kim, Junyeob Kim, Hyuhng Joon Yoo, Kang Min Lee, Sang-goo
Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning