Leveraging Large and Vision Language Models for Gloss-Free Sign Language Translation in Deaf Communication: A Survey

Leveraging Large and Vision Language Models for Gloss-Free Sign Language Translation in Deaf Communication: A Survey | 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 Leveraging Large and Vision Language Models for Gloss-Free Sign Language Translation in Deaf Communication: A Survey Sufyan Danish, Samee Ullah Khan, Norah Saleh Alghamdi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7840570/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Sign Language Translation (SLT) is an essential tool for overcoming communication barriers faced by the Deaf and Hard-of-Hearing (DHH) community, enabling equal access to vital information and interactions. This need is especially critical in large-scale, multilingual settings such as Hajj and Umrah, where millions of pilgrims, including DHH individuals, gather annually. Despite its significance, SLT remains challenging due to the multimodal nature of sign languages, which differ significantly from spoken languages. Although earlier surveys have reviewed foundational techniques such as gloss-based translation, attention mechanisms, and early machine learning models, they do not address the transformative impact of vision-language models and multimodal large-language models. This survey fills this gap by systematically reviewing state-of-the-art approaches enabled by Artificial Intelligence in SLT, facilitating seamless interaction between DHH and hearing individuals. We synthesize advancements across three key architectural paradigms: adapter-based language models, hierarchical tokenization frameworks, and vision-language pretraining. We also evaluate modern datasets and emerging evaluation metrics while comparing benchmark scores of the latest gloss-free frameworks. Furthermore, we explore current knowledge-based systems designed to assist DHH individuals, along with their limitations. By integrating these contributions, this survey advances SLT research and provides a roadmap for future innovations in low-resource adaptation, ethical AI development, and global accessibility initiatives. Sign Language Translation Arabic Sign Language Multimodal LLMs Vision-Language Models Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 23 Jan, 2026 Reviews received at journal 22 Jan, 2026 Reviews received at journal 19 Jan, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviews received at journal 08 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers invited by journal 07 Jan, 2026 Editor assigned by journal 16 Oct, 2025 Submission checks completed at journal 14 Oct, 2025 First submitted to journal 12 Oct, 2025 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. 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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-7840570","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571215453,"identity":"d34ecb7a-19a1-4e4c-903f-42431dd83cf4","order_by":0,"name":"Sufyan Danish","email":"","orcid":"","institution":"Sejong University","correspondingAuthor":false,"prefix":"","firstName":"Sufyan","middleName":"","lastName":"Danish","suffix":""},{"id":571215455,"identity":"0e71e6ed-bc3d-455d-bf59-d85b4e1252f0","order_by":1,"name":"Samee Ullah 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