{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/superglue-a-stickier-benchmark-for-general","title":"SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems","arxiv_id":"1905.00537","date":"2019-05-02","proceeding":"NeurIPS 2019 12","authors":["Alex Wang","Yada Pruksachatkun","Nikita Nangia","Amanpreet Singh","Julian Michael","Felix Hill","Omer Levy","Samuel R. Bowman"],"abstract":"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.","url_abs":"https://arxiv.org/abs/1905.00537v3","url_pdf":"https://arxiv.org/pdf/1905.00537v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"superglue-a-stickier-benchmark-for-general","repo_url":"https://github.com/nyu-mll/jiant","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"superglue-a-stickier-benchmark-for-general","repo_url":"https://github.com/DataScienceNigeria/SUPERGLUE-from-Facebook-AI-DeepMind-University-of-Washington-and-New-York-University.","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"superglue-a-stickier-benchmark-for-general","repo_url":"https://github.com/colinzhaoust/intrinsic_fewshot_hardness","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"superglue-a-stickier-benchmark-for-general","repo_url":"https://github.com/debugml/incontext_influences","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"superglue-a-stickier-benchmark-for-general","repo_url":"https://github.com/google-research/prompt-tuning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"superglue-a-stickier-benchmark-for-general","repo_url":"https://github.com/ledzy/badam","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[{"slug":"superglue","name":"SuperGLUE","full_name":"SuperGLUE"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1905.00537","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}