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Computational complexity and overthinking problems have become thebottlenecks for pre-training language models (PLMs) with millions or eventrillions of parameters. A Flexible-Patience-Based Early Exiting method(F-PABEE) has been proposed to alleviate the problems mentioned above forsingle-label classification (SLC) and multi-label classification (MLC) tasks.F-PABEE makes predictions at the classifier and will exit early if predicteddistributions of cross-layer are consecutively similar. It is more flexiblethan the previous state-of-the-art (SOTA) early exiting method PABEE because itcan simultaneously adjust the similarity score thresholds and the patienceparameters. Extensive experiments show that: (1) F-PABEE makes a betterspeedup-accuracy balance than existing early exiting strategies on both SLC andMLC tasks. (2) F-PABEE achieves faster inference and better performances ondifferent PLMs such as BERT and ALBERT. (3) F-PABEE-JSKD performs best forF-PABEE with different similarity measures.

F-PABEE: Flexible-patience-based Early Exiting for Single-label and Multi-label text Classification Tasks