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Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Supersized pre-trained language models have pushed the accuracy of various natural language processing (NLP) tasks to a new stateof-the-art (SOTA). Rather than pursuing the reachless SOTA accuracy, more and more researchers start paying attention to model efficiency and usability. Different from accuracy, the metric for efficiency varies across different studies, making them hard to be fairly compared. To that end, this work presents ELUE (Efficient Language Understanding Evaluation), adoi:10.18653/v1/2022.naacl-main.240 fatcat:c4xjrckk7fh2bi7ktsvoffr5xi