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SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive licence to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/1905.00537

Summary

By mid-2019, pretrained transformer models had pushed the GLUE benchmark past its estimate of non-expert human performance, so GLUE could no longer track real progress in language understanding. The authors respond with SuperGLUE, a successor benchmark that keeps GLUE's basic setup: one aggregate score, a public leaderboard, and diagnostic data. It replaces the task set with eight harder English problems. Two tasks carried over from GLUE, and the rest were picked from an open call for proposals based on how hard they were for existing systems. The task formats now go beyond sentence classification to include coreference and question answering. Every task comes with a human performance estimate, gathered partly through new crowdsourced annotation. The release also includes a modular toolkit built on PyTorch and AllenNLP, along with stricter leaderboard rules. BERT-based baselines remain far below humans on SuperGLUE, while BERT had already approached human level on GLUE. This gap gives multi-task, transfer, and self-supervised learning methods a meaningful target again.

Key points

  • The trigger: in July 2019, the top GLUE score of 88.4 (XLNet) was 1.3 points higher than the human estimate of 87.1.
  • The eight SuperGLUE tasks are BoolQ, CB, COPA, MultiRC, ReCoRD, RTE, WiC and WSC. They cover QA, NLI, word sense disambiguation and coreference. Training data is small: almost half the tasks have fewer than 1k training examples, and all except ReCoRD have fewer than 10k.
  • Fine-tuned BERT scores about 25 points above the majority-class and CBOW (GloVe) baselines on average. On WSC, though, BERT does worse than these simple baselines.
  • BERT++ adds intermediate training before fine-tuning: MultiNLI for BoolQ, CB and RTE (a gain of 2–5 points) and SWAG for COPA (a gain of 8 points). It still trails humans by close to 20 points on average, and by 35 points on WSC.
  • Human accuracy is perfect on WSC and COPA. Models reach near-perfect gender parity on Winogender only because their accuracy there is close to random guessing.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

1221 papers of our library cite it (of the 140673 whose bibliographies we read); 106 of them have a page here, the first 100 below, 1115 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.1905.00537 · arXiv 1905.00537

@misc{wang2019superglue,
  title = {{SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems}},
  author = {Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  year = {2019},
  doi = {10.48550/arxiv.1905.00537},
  eprint = {1905.00537},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1905.00537}
}

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  74. Table 4: Baseline performance on the SuperGLUE development. Model Metrics Avg BoolQ Acc. CB Acc./F1 Most Frequent Class 47.7 CBOW 47.7 BERT 72.2 BERT++ 74.6 62.2 62.4 77.7 80.1 50.0/22.2 71.4/49.6 94.6/93.7 96.4/95.0 find