{"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/how-certain-is-your-transformer","title":"How Certain is Your Transformer?","arxiv_id":null,"date":"2021-04-01","proceeding":"EACL 2021 2","authors":["Artem Shelmanov","Evgenii Tsymbalov","Dmitri Puzyrev","Kirill Fedyanin","Alexander Panchenko","Maxim Panov"],"abstract":"In this work, we consider the problem of uncertainty estimation for Transformer-based models. We investigate the applicability of uncertainty estimates based on dropout usage at the inference stage (Monte Carlo dropout). The series of experiments on natural language understanding tasks shows that the resulting uncertainty estimates improve the quality of detection of error-prone instances. Special attention is paid to the construction of computationally inexpensive estimates via Monte Carlo dropout and Determinantal Point Processes.","url_abs":"https://aclanthology.org/2021.eacl-main.157","url_pdf":"https://aclanthology.org/2021.eacl-main.157.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":"how-certain-is-your-transformer","repo_url":"https://github.com/skoltech-nlp/certain-transformer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"},{"task_slug":"point-processes","task_name":"Point Processes"}],"methods":[{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"monte-carlo-dropout","method_name":"Monte Carlo Dropout"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}