Adaptive Query Contextualization Algorithm for Enhanced Information Retrieval in Alpaca LLM

Adaptive Query Contextualization Algorithm for Enhanced Information Retrieval in Alpaca LLM | 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 Adaptive Query Contextualization Algorithm for Enhanced Information Retrieval in Alpaca LLM Chih-Wei Kuo, Yueh-Fen Huang, Hsiao-Ching Tsai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3806145/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 This study focused on the development and evaluation of an Adaptive Query Contextualization Algorithm (AQCA) within the Alpaca Large Language Model (LLM) framework. The AQCA was designed to enhance the model's capability in information retrieval by employing a novel context encoding methodology that dynamically adapted to multifaceted contextual signals derived from user search history and interaction patterns. The algorithm's efficacy was rigorously tested across various metrics, including Contextual Relevance Score (CRS), Word Prediction Accuracy (WPA), Information Retrieval Fidelity (IRF), and Response Coherence Measure (RCM). Significant improvements were observed in the augmented Alpaca LLM's performance, especially in complex scenarios such as metaphorical language understanding and domain-specific knowledge integration. Challenges related to scalability, adaptability to multilingual contexts, and integration with diverse LLM architectures were identified, emphasizing the need for continued research in these areas. The study concluded that while the AQCA marked a substantial advancement in LLMs for context-aware information retrieval, it also opened avenues for future innovations focusing on technical enhancements and ethical considerations. Artificial Intelligence and Machine Learning Adaptive Query Contextualization Large Language Models Information Retrieval Context Encoding Algorithm Evaluation Multilingual Context Awareness 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-3806145","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":263246447,"identity":"f82a60e3-1574-4626-bfac-ff99b0a1bc11","order_by":0,"name":"Chih-Wei Kuo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIie3RMWrDMBiG4U8Y0kWNV5uAewWVQqdAr2JjcJaYZioeBQF7aEpW+xbpDWQ0ZFEyG1JoRaFzIGuHWmmGDg7x2EHv8GMJPyAhwGb7jwnAAWsnCCccCMxXuwz7kzuz7kHMPCIg4pfIcCfIYTZ7A1vPOany8WTpPQqxzxK4xXOn87ehMyrZF5iqOXnNk7QqY9SlmsJTm1UXYQoDhzKJ+ybiROcyXTUx5HWegXnpBfKuj2TC+pOGmIPJ8ESmZ4mvyHzUEvqgIl6X2+S2Wnyy9i4JPXeXoXLqA/2WgV+stV48jW/cq0h/7LM4cIuXTvL7GAA1Q/zdp92/22w2m61PP1iGaG2fUimYAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chih-Wei","middleName":"","lastName":"Kuo","suffix":""},{"id":263246448,"identity":"3331c300-aa84-4a8d-ae9d-4c4932461d33","order_by":1,"name":"Yueh-Fen Huang","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yueh-Fen","middleName":"","lastName":"Huang","suffix":""},{"id":263246449,"identity":"22a23e4f-c25b-403b-9e4a-a3759d8880d9","order_by":2,"name":"Hsiao-Ching Tsai","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hsiao-Ching","middleName":"","lastName":"Tsai","suffix":""}],"badges":[],"createdAt":"2023-12-26 03:48:53","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-3806145/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3806145/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":48848364,"identity":"358d4701-fd19-470c-a84b-ddd1fe5c30a0","added_by":"auto","created_at":"2023-12-27 09:28:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":723410,"visible":true,"origin":"","legend":"","description":"","filename":"article.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3806145/v1_covered_26eb90bd-9d1f-4e12-86ad-59913f6fd716.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAdaptive Query Contextualization Algorithm for Enhanced Information Retrieval in Alpaca LLM\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Kaohsiung Advanced Artificial Intelligence Research Center","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":"Adaptive Query Contextualization, Large Language Models, Information Retrieval, Context Encoding, Algorithm Evaluation, Multilingual Context Awareness","lastPublishedDoi":"10.21203/rs.3.rs-3806145/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3806145/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study focused on the development and evaluation of an Adaptive Query Contextualization Algorithm (AQCA) within the Alpaca Large Language Model (LLM) framework. 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