Dynamic Token Expansion through Contextual Morphogenesis in Large Language Models

Dynamic Token Expansion through Contextual Morphogenesis in Large Language Models | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 19 November 2024 V1 Latest version Share on Dynamic Token Expansion through Contextual Morphogenesis in Large Language Models Authors : Sveta Glinnikova 0009-0007-3782-0650 [email protected] , Patrick Young , Matthew Marchand , Christopher Vandenberg , and Roderick Stellanova Authors Info & Affiliations https://doi.org/10.22541/au.173204391.15306634/v1 236 views 166 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The rapid growth in textual data and the increasing complexity of linguistic patterns have demanded more sophisticated approaches to tokenization and contextual understanding within language models. Traditional tokenization methods, constrained by static segmentation, fail to address the dynamic and context-dependent nature of human language, limiting their ability to fully capture semantic relationships. The Dynamic Token Expansion framework introduces a paradigm shift through its context-aware mechanism, enabling token boundaries to morph dynamically during runtime, thereby bridging the gap between rigid preprocessing techniques and the fluid nature of language. Experimental evaluations demonstrate significant improvements in tokenization accuracy, model performance in domain-specific applications, and user engagement metrics, showing the framework's adaptability and robustness. By integrating this novel approach into open-source language models, the study highlights transformative implications for linguistic adaptability, efficiency, and the broader application potential of advanced tokenization strategies. The findings establish a foundational step toward the development of more context-sensitive and semantically aware natural language systems. Supplementary Material File (58330.pdf) Download 194.52 KB Information & Authors Information Version history V1 Version 1 19 November 2024 Copyright This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License Keywords advanced nlp computational linguistics context-awareness linguistic adaptability model optimization tokenization Authors Affiliations Sveta Glinnikova 0009-0007-3782-0650 [email protected] View all articles by this author Patrick Young View all articles by this author Matthew Marchand View all articles by this author Christopher Vandenberg View all articles by this author Roderick Stellanova View all articles by this author Metrics & Citations Metrics Article Usage 236 views 166 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Sveta Glinnikova, Patrick Young, Matthew Marchand, et al. Dynamic Token Expansion through Contextual Morphogenesis in Large Language Models. Authorea . 19 November 2024. 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