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In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. The GLUE benchmark, introduced a little over one year ago, offers a single-number metric that summarizes progress on a diverse set of such tasks, but performance on the benchmark has recently surpassed the level of non-expert humans, suggesting limited headroom for further research. In this paper we present SuperGLUE, a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, a software toolkit, and a public leaderboard. SuperGLUE is available at super.gluebenchmark.com.
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Wang et al. (Thu,) studied this question.
www.synapsesocial.com/papers/6a07fa897ad161a3abfe0ef3 — DOI: https://doi.org/10.48550/arxiv.1905.00537
Alex Wang
Yada Pruksachatkun
Nikita Nangia
University of Washington
Meta (Israel)
DeepMind (United Kingdom)
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