An Intelligent-Aware Transformer with Domain Adaptation and Contextual Reasoning for Question Answering

An Intelligent-Aware Transformer with Domain Adaptation and Contextual Reasoning for Question Answering | 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 An Intelligent-Aware Transformer with Domain Adaptation and Contextual Reasoning for Question Answering Jianyang Zhuo, Yuchen Han, Hairu Wen, Kejian Tong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6883521/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 With the rapid growth of financial data, extracting accurate and contextually relevant information remains a challenge. Existing financial question-answering (QA) models struggle with domain-specific terminology, long-document processing, and answer consistency. To address these issues, this paper proposes the Intelligent-Aware Transformer (IAT), a financial QA system based on GLM4-9B-Chat, integrating a multi-level information aggregation framework. The system employs a Financial-Specific Attention Mechanism (FSAM) to enhance focus on key financial terms, a Dynamic Context Embedding Layer (DCEL) to improve long-document processing, and a Hierarchical Answer Aggregator (HAA) to ensure response coherence. Additionally, Knowledge-Augmented Textual Entailment (KATE) strengthens the model’s generalization by inferring implicit financial knowledge. Experimental results demonstrate that IAT surpasses existing models in financial QA tasks, exhibiting superior adaptability in long-text comprehension and domain-specific reasoning. Future work will explore computational optimizations, advanced knowledge integration, and broader financial applications. Artificial Intelligence and Machine Learning Financial Question Answering Transformer Models Knowledge Augmentation Financial NLP Deep Learning 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-6883521","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470611076,"identity":"b2e6c5ec-7b83-4636-9fa2-325754b13bba","order_by":0,"name":"Jianyang Zhuo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACxmYYSwKIPwAxGzspWhhngLQwE20fUAszD4hBSAtzO/MziZ87ahnkZ/cYPrb5tU2ej5mB8cPHHHwOYzOT7D1znIFxzhlj49y+24ZtzAzMkjO34fWLmQRv2zEGZokcM+ncntuMQC1szLx4tbB/k/wL1MIG0mLZc9ueCC08ZtK8bTUMPCAtDD9uJxKjpdhatu0Aj4REWrFhb8Pt5DZmxma8fjHsP77x5tu2Ojn5GckbH/z4c9t2fnvzwQ8f8WlpYGABxshhHoidbWCyAbd6IJAHRg0wmdRBuX/wKh4Fo2AUjIIRCgDLykhqiQrfegAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Jianyang","middleName":"","lastName":"Zhuo","suffix":""},{"id":470611077,"identity":"1d0e30fa-d994-43d3-a28a-a005c0edc9a5","order_by":1,"name":"Yuchen Han","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yuchen","middleName":"","lastName":"Han","suffix":""},{"id":470611078,"identity":"b88a9f1e-1f93-49e6-af7a-49be17caca76","order_by":2,"name":"Hairu Wen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hairu","middleName":"","lastName":"Wen","suffix":""},{"id":470611079,"identity":"2f8a00e7-9a68-4538-8c55-6d796135585c","order_by":3,"name":"Kejian Tong","email":"","orcid":"https://orcid.org/0009-0002-5127-2711","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kejian","middleName":"","lastName":"Tong","suffix":""}],"badges":[],"createdAt":"2025-06-13 00:18:23","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-6883521/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6883521/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84660760,"identity":"ece4c378-fb62-4bfe-80da-6debd576157c","added_by":"auto","created_at":"2025-06-16 04:22:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":750073,"visible":true,"origin":"","legend":"","description":"","filename":"AnIntelligentAwareTransformerwithDomainAdaptationandContextualReasoningforQuestionAnswering1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6883521/v1_covered_ec109415-a9cc-4912-94a6-28684e920cf6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cem\u003eAn Intelligent-Aware Transformer with Domain Adaptation and Contextual Reasoning for Question Answering\u003c/em\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"Financial Question Answering, Transformer Models, Knowledge Augmentation, Financial NLP, Deep Learning","lastPublishedDoi":"10.21203/rs.3.rs-6883521/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6883521/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eWith the rapid growth of financial data, extracting accurate and contextually relevant information remains a challenge. Existing financial question-answering (QA) models struggle with domain-specific terminology, long-document processing, and answer consistency. To address these issues, this paper proposes the Intelligent-Aware Transformer (IAT), a financial QA system based on GLM4-9B-Chat, integrating a multi-level information aggregation framework. The system employs a Financial-Specific Attention Mechanism (FSAM) to enhance focus on key financial terms, a Dynamic Context Embedding Layer (DCEL) to improve long-document processing, and a Hierarchical Answer Aggregator (HAA) to ensure response coherence. Additionally, Knowledge-Augmented Textual Entailment (KATE) strengthens the model’s generalization by inferring implicit financial knowledge. Experimental results demonstrate that IAT surpasses existing models in financial QA tasks, exhibiting superior adaptability in long-text comprehension and domain-specific reasoning. 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