Fake News Detection with Large Language Models on the LIAR Dataset

Fake News Detection with Large Language Models on the LIAR Dataset | 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 Fake News Detection with Large Language Models on the LIAR Dataset David Boissonneault, Emily Hensen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4465815/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The widespread dissemination of fake news poses a significant threat to the integrity of information. Detecting fake news with high accuracy is crucial for maintaining the integrity of information in the digital age. The evaluation of ChatGPT and Google Gemini models for this task has revealed their substantial capabilities in discerning the veracity of statements, highlighting their potential to mitigate the spread of misinformation. Using the LIAR benchmark dataset, the study demonstrated high performance metrics across accuracy, precision, recall, F1 score, and AUC-ROC, emphasizing the effectiveness of these models in real-world applications. The comparative analysis and error examination provided insights into the strengths and limitations of each model, offering valuable guidance for future enhancements. Practical implications include the integration of these models into fact-checking systems to improve content verification processes, supporting media organizations and social platforms in their efforts to combat misinformation. The findings prove the importance of ongoing research and development to refine and optimize LLMs, ensuring their continued relevance and efficacy in addressing the challenges posed by fake news. Artificial Intelligence and Machine Learning Fake news detection LIAR dataset Fact-checking Misinformation Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4465815","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305866692,"identity":"f9e8bd46-696e-4f19-9b51-f3b26dc32478","order_by":0,"name":"David Boissonneault","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0004-7660-2275","institution":"Tessellate AI","correspondingAuthor":true,"prefix":"","firstName":"David","middleName":"","lastName":"Boissonneault","suffix":""},{"id":305866693,"identity":"8e8ec12b-a6ad-41a1-89f3-3277986bd48c","order_by":1,"name":"Emily Hensen","email":"","orcid":"https://orcid.org/0009-0001-3273-5891","institution":"Tessellate AI","correspondingAuthor":false,"prefix":"","firstName":"Emily","middleName":"","lastName":"Hensen","suffix":""}],"badges":[],"createdAt":"2024-05-23 09:35:10","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4465815/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4465815/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57054691,"identity":"8c786bce-e148-47e7-ae64-dca00de00ef2","added_by":"auto","created_at":"2024-05-24 04:23:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":187339,"visible":true,"origin":"","legend":"","description":"","filename":"rs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4465815/v1_covered_c64bd07d-ce16-4c3a-9b01-eade85fcdb6d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eFake News Detection with Large Language Models on the LIAR Dataset\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Tessellate AI","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":" Fake news detection, LIAR dataset, Fact-checking, Misinformation","lastPublishedDoi":"10.21203/rs.3.rs-4465815/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4465815/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe widespread dissemination of fake news poses a significant threat to the integrity of information. Detecting fake news with high accuracy is crucial for maintaining the integrity of information in the digital age. The evaluation of ChatGPT and Google Gemini models for this task has revealed their substantial capabilities in discerning the veracity of statements, highlighting their potential to mitigate the spread of misinformation. Using the LIAR benchmark dataset, the study demonstrated high performance metrics across accuracy, precision, recall, F1 score, and AUC-ROC, emphasizing the effectiveness of these models in real-world applications. The comparative analysis and error examination provided insights into the strengths and limitations of each model, offering valuable guidance for future enhancements. Practical implications include the integration of these models into fact-checking systems to improve content verification processes, supporting media organizations and social platforms in their efforts to combat misinformation. The findings prove the importance of ongoing research and development to refine and optimize LLMs, ensuring their continued relevance and efficacy in addressing the challenges posed by fake news.\u003c/p\u003e","manuscriptTitle":"Fake News Detection with Large Language Models on the LIAR Dataset","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-24 04:15:16","doi":"10.21203/rs.3.rs-4465815/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f9c8ed80-6463-4387-8ea5-934a11a57632","owner":[],"postedDate":"May 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32301600,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2024-05-24T04:15:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-24 04:15:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4465815","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4465815","identity":"rs-4465815","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}