Large Language Models Augment or Substitute Human Experts in Idea Screening

Large Language Models Augment or Substitute Human Experts in Idea Screening | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Large Language Models Augment or Substitute Human Experts in Idea Screening Brendon Rhodes, Pavel Kireyev, Cathy Yang, Abhishek Borah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7085870/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Firms that use crowdsourcing to gather advertising and product ideas often rely on internal experts to manually screen thousands of submissions, a costly and time-consuming process. Internal experts rate thousands of ideas to identify a small set of promising ones that are then submitted for additional review. We evaluate how large language models (LLMs), when combined with a machine learning model trained on historical expert ratings and final client selections, can improve the efficiency of this screening. Using data from a platform that engaged experts to evaluate 74,436 ideas across 153 contests for major advertisers, we show that evaluation effort can be reduced by 28.4% compared to the status quo. Of this reduction, 3.8% is directly attributable to the LLM output, while the remainder comes from better weighting expert scores to align with sponsor preferences. Notably, incorporating LLMs could make 5 out of 10 experts redundant, compared to 3 with machine learning alone. Importantly, the experts whose judgments are most replicable by the LLM are not necessarily the poorest performers. These findings offer a practical framework for integrating LLMs into idea screening pipelines and underscore their potential to streamline expert evaluation while maintaining alignment with client goals. Ideation Idea Screening Crowdsourcing LLMs Prompt Engineering Machine Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Oct, 2025 Reviews received at journal 05 Oct, 2025 Reviews received at journal 23 Sep, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers invited by journal 20 Aug, 2025 Editor assigned by journal 14 Aug, 2025 Submission checks completed at journal 14 Jul, 2025 First submitted to journal 09 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7085870","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":503900486,"identity":"8cacdce2-17b5-4fce-901a-0a8ccbe4c5db","order_by":0,"name":"Brendon Rhodes","email":"","orcid":"","institution":"INSEAD","correspondingAuthor":false,"prefix":"","firstName":"Brendon","middleName":"","lastName":"Rhodes","suffix":""},{"id":503900487,"identity":"a38ba5cf-60c5-4e4a-9502-d4a3820fcf94","order_by":1,"name":"Pavel Kireyev","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYFACNoYDYJq9uQEuQqQWnoMkaIEAiUQitZgzsCUerqg4LGdw82Hzhw8MdvIMEmkJeLVYNrAdOHjmzGFjg9uJbZIzGJINGyTSDuDVYnCAveFgY9vhxJmzE9uYeRiYExgk0huI0PLvcP3MmQebP/9hqCdGC9BhjQ2HE/glGBukGRgOA7UQcJhlM1vCwYZj6Yb9PEC/9BgcN2zjeZaAV4s5e5vxx4Yaa3k29sOHP/yoqJbnZ08zwO8wZjDVDOMSEZFQA+sIqRsFo2AUjIKRDABT+0VVV9qX3QAAAABJRU5ErkJggg==","orcid":"","institution":"London School of Economics and Political Science","correspondingAuthor":true,"prefix":"","firstName":"Pavel","middleName":"","lastName":"Kireyev","suffix":""},{"id":503900488,"identity":"f5c069ea-8e7a-44c9-97a4-6faf441e1c39","order_by":2,"name":"Cathy Yang","email":"","orcid":"","institution":"Hautes Études Commerciales de Paris","correspondingAuthor":false,"prefix":"","firstName":"Cathy","middleName":"","lastName":"Yang","suffix":""},{"id":503900489,"identity":"7976677b-a950-4581-977e-a9683cff9e51","order_by":3,"name":"Abhishek Borah","email":"","orcid":"","institution":"INSEAD","correspondingAuthor":false,"prefix":"","firstName":"Abhishek","middleName":"","lastName":"Borah","suffix":""}],"badges":[],"createdAt":"2025-07-09 16:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7085870/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7085870/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[],"financialInterests":"No competing interests reported.","formattedTitle":"Large Language Models Augment or Substitute Human Experts in Idea Screening","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"quantitative-marketing-and-economics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"qmec","sideBox":"Learn more about [Quantitative Marketing and Economics](http://link.springer.com/journal/11129)","snPcode":"11129","submissionUrl":"https://submission.springernature.com/new-submission/11129/3","title":"Quantitative Marketing and Economics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ideation, Idea Screening, Crowdsourcing, LLMs, Prompt Engineering, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-7085870/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7085870/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFirms that use crowdsourcing to gather advertising and product ideas often rely on internal experts to manually screen thousands of submissions, a costly and time-consuming process. 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