Linguistic Precision in Large Language Models with Adaptive Disambiguation and Efficient Monte Carlo Tree Search for Contextual Clarity

Linguistic Precision in Large Language Models with Adaptive Disambiguation and Efficient Monte Carlo Tree Search for Contextual Clarity | 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 Linguistic Precision in Large Language Models with Adaptive Disambiguation and Efficient Monte Carlo Tree Search for Contextual Clarity Evan Geline, Robert Evans, Elizabeth Russo, Daniel Richardson, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5186782/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 ability to accurately interpret ambiguous linguistic structures has long been a fundamental challenge in constructing models capable of generating human-like text. Standard approaches to word sense disambiguation have traditionally struggled to maintain precision in contexts where words can carry multiple meanings depending on surrounding content. Introducing an adaptive disambiguation mechanism, enhanced through the integration of Monte Carlo Tree Search (MCTS), allows for real-time exploration of word sense possibilities and the dynamic refinement of interpretations as additional contextual information becomes available. This combination of probabilistic weighting and structured search enables the model to prioritize the most relevant meanings while efficiently managing computational resources, making it highly adaptable to complex language tasks. A modified Llama model incorporating this approach demonstrated significant improvements in disambiguation accuracy across a range of linguistic benchmarks, including multilingual and domain-specific tasks. Experimental results confirm the effectiveness of MCTS in balancing the trade-offs between linguistic precision and computational efficiency, providing a scalable solution to challenges in understanding and generating contextually accurate language. Artificial Intelligence and Machine Learning disambiguation MCTS contextual accuracy probabilistic models linguistic precision 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-5186782","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":361076308,"identity":"23527a60-295c-40a9-b120-a2975f1b41b7","order_by":0,"name":"Evan 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