Predicting Fine-Tuning Performance with Probing

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11534 - 11547 December 7-11, 2022 ©2022 Association for Computational Linguistics Predicting Fine-Tuning Performance with Probing Zining Zhu1,2, Soroosh Shahtalebi2, Frank Rudzicz1,2,3 1 University of Toronto 2 Vector Institute for Artificial Intelligence 3 Unity Health Toronto zining@cs.toronto.edu, soroosh.shahtalebi@vectorinstitute.ai frank@spoclab.com Abstract Large NLP models have recently shown im- pressive performance in language understand- ing tasks, typically evaluated by their fine- tuned performance. Alternatively, probing has received increasing attention as being a lightweight method for interpreting the intrin- sic mechanisms of large NLP models. In prob- ing, post-hoc classifiers are trained on “out-of- domain” datasets that diagnose specific abili- ties. 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 mod- els to extract a proxy signal widely used in model development – the fine-tuning perfor- mance. 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. 1 Introduction Large-scale neural models have recently demon- strated state-of-the-art performance in a wide va- riety of tasks, including sentiment detection, para- phrase detection, linguistic acceptability, and en- tailment detection (Devlin et al., 2019; Radford et al., 2019; Peters et al., 2018). Developing sys- tems for these tasks usually involves two stages: a pre-training stage, where the large neural models gain linguistic knowledge from weak supervision signals in massive corpora, and a fine-tuning stage, where the models acquire task-specific knowledge from labeled data. The fine-tuning results are widely used to benchmark the performances of neural models and refine the models’ development procedures. However, these fine-tuning results are summary statistics and do not paint the full picture of deep neural models (Ethayarajh and Jurafsky, 2020; Ben- der and Koller, 2020). As researchers are increas- ingly concerned about interpreting the intrinsic mechanisms of deep neural models, many data- driven assessment methods have been developed. These assessments usually follow the route of com- piling a targeted dataset and running post-hoc anal- yses. Until now, one of the most popular inter- pretation methods is referred to as probing. To probe a neural model, one uses a predictor to obtain the labels from the representations that are embed- ded using the neural model. Probing analyses on deep neural models revealed some low-dimensional syntactic structures (Hewitt and Manning, 2019), common-sense knowledge (Petroni et al., 2019) and (to some extent) human-like abilities, including being surprised upon witnessing linguistic irregu- larity (Li et al., 2021) and reasoning about space and time (Aroca-Ouellette et al., 2021). From the viewpoint of data-driven assessments, both fine-tuning and probing can reveal the abilities of deep neural networks, but they appear to steer towards different directions: In-domain vs. out-of-domain. Fine-tuning uses in-domain data – we evaluate the models on the same distributions as those in …

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