Efficiently Updating Domain Knowledge in Large Language Models: Techniques for Knowledge Injection without Comprehensive Retraining

Efficiently Updating Domain Knowledge in Large Language Models: Techniques for Knowledge Injection without Comprehensive Retraining | 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 Efficiently Updating Domain Knowledge in Large Language Models: Techniques for Knowledge Injection without Comprehensive Retraining Emily Czekalski, David Watson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4533670/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 Recent advancements in natural language processing have highlighted the critical importance of efficiently updating pre-trained models with domain-specific knowledge. Traditional methods requiring comprehensive retraining are resource-intensive and impractical for many applications. The proposed techniques for knowledge injection, including the integration of adapter layers, retrieval-augmented generation (RAG), and knowledge distillation, offer a novel and significant solution to this challenge by enabling efficient updates without extensive retraining. Adapter layers allow for specialized fine-tuning, preserving the model's original capabilities while incorporating new information. RAG enhances the contextual relevance of generated responses by dynamically retrieving pertinent information from a domain-specific knowledge base. Knowledge distillation transfers specialized knowledge from smaller models to the larger pre-trained model, augmenting its performance in new domains. Experimental results demonstrated substantial improvements in accuracy, precision, recall, and F1-score, along with enhanced contextual relevance and coherence. The findings demonstrate the potential of the proposed methods to maintain the relevance and accuracy of language models in dynamic, information-rich environments, making them particularly useful in fields requiring timely and accurate information. Artificial Intelligence and Machine Learning Knowledge injection Adapter layers Retrieval-augmented generation Knowledge distillation Domain-specific Natural language processing 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. 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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-4533670","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310873108,"identity":"6c767689-55b3-4a10-9870-4efb31fcb855","order_by":0,"name":"Emily Czekalski","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0009-2323-3636","institution":"Vantage AI","correspondingAuthor":true,"prefix":"","firstName":"Emily","middleName":"","lastName":"Czekalski","suffix":""},{"id":310873109,"identity":"148b865a-feeb-40d8-821d-7b14d27314cf","order_by":1,"name":"David Watson","email":"","orcid":"https://orcid.org/0009-0009-3064-4767","institution":"Vantage AI","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Watson","suffix":""}],"badges":[],"createdAt":"2024-06-05 11:24:29","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-4533670/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4533670/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57813663,"identity":"60fe5d32-f1d8-4a59-a540-72fddf717800","added_by":"auto","created_at":"2024-06-06 03:41:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":196402,"visible":true,"origin":"","legend":"","description":"","filename":"tech.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4533670/v1_covered_be85f9d2-8d86-4d16-8142-ee8b015a39c9.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEfficiently Updating Domain Knowledge in Large Language Models: Techniques for Knowledge Injection without Comprehensive Retraining\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Vantage 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":"Knowledge injection, Adapter layers, Retrieval-augmented generation, Knowledge distillation, Domain-specific, Natural language processing","lastPublishedDoi":"10.21203/rs.3.rs-4533670/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4533670/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecent advancements in natural language processing have highlighted the critical importance of efficiently updating pre-trained models with domain-specific knowledge. 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