{"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/showing-your-work-doesn-t-always-work","title":"Showing Your Work Doesn't Always Work","arxiv_id":"2004.13705","date":"2020-04-28","proceeding":"ACL 2020 6","authors":["Raphael Tang","Jaejun Lee","Ji Xin","Xinyu Liu","Yao-Liang Yu","Jimmy Lin"],"abstract":"In natural language processing, a recently popular line of work explores how to best report the experimental results of neural networks. One exemplar publication, titled \"Show Your Work: Improved Reporting of Experimental Results,\" advocates for reporting the expected validation effectiveness of the best-tuned model, with respect to the computational budget. In the present work, we critically examine this paper. As far as statistical generalizability is concerned, we find unspoken pitfalls and caveats with this approach. We analytically show that their estimator is biased and uses error-prone assumptions. We find that the estimator favors negative errors and yields poor bootstrapped confidence intervals. We derive an unbiased alternative and bolster our claims with empirical evidence from statistical simulation. Our codebase is at http://github.com/castorini/meanmax.","url_abs":"https://arxiv.org/abs/2004.13705v1","url_pdf":"https://arxiv.org/pdf/2004.13705v1.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":"showing-your-work-doesn-t-always-work","repo_url":"https://github.com/castorini/meanmax","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2004.13705","atlas_url":"https://app.syntology.ai/?focus=2004.13705","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}