@inproceedings{zhu-etal-2022-predicting,
    title = "Predicting Fine-Tuning Performance with Probing",
    author = "Zhu, Zining  and
      Shahtalebi, Soroosh  and
      Rudzicz, Frank",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.emnlp-main.793/",
    doi = "10.18653/v1/2022.emnlp-main.793",
    pages = "11534--11547",
    abstract = "Large NLP models have recently shown impressive performance in language understanding tasks, typically evaluated by their fine-tuned performance. Alternatively, probing has received increasing attention as being a lightweight method for interpreting the intrinsic mechanisms of large NLP models. In probing, post-hoc classifiers are trained on ``out-of-domain'' datasets that diagnose specific abilities. While probing the language models has led to insightful findings, they appear disjointed from the development of models. This paper explores the utility of probing deep NLP models to extract a proxy signal widely used in model development {--} the fine-tuning performance. We find that it is possible to use the accuracies of only three probing tests to predict the fine-tuning performance with errors 40{\%} - 80{\%} smaller than baselines. We further discuss possible avenues where probing can empower the development of deep NLP models."
}