Beyond Words: The Evolution of Large Language Models in Context-Aware

Beyond Words: The Evolution of Large Language Models in Context-Aware | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 3 March 2025 V1 Latest version Share on Beyond Words: The Evolution of Large Language Models in Context-Aware Author : William Jack 0009-0001-4899-8362 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174102439.94682577/v1 341 views 168 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Large Language Models (LLMs) have revolutionized artificial intelligence by enabling sophisticated natural language understanding, generation, and reasoning. As these models evolve, context awareness has become a crucial factor in improving their adaptability, coherence, and decision-making capabilities. This paper explores the evolution of LLMs from rule-based systems to deep learning architectures, emphasizing advancements in contextual embeddings, multimodal learning, and real-time adaptation. We discuss the impact of reinforcement learning with human feedback (RLHF), memory-augmented models, and transformer-based architectures in enhancing contextual sensitivity. Additionally, ethical concerns such as bias, misinformation, and privacy risks are examined alongside mitigation strategies. The paper concludes by assessing future trends, including the integration of LLMs with edge AI, federated learning, and knowledge graphs to achieve more reliable, efficient, and human-aligned AI systems. Supplementary Material File (2.pdf) Download 233.23 KB Information & Authors Information Version history V1 Version 1 03 March 2025 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords context awareness large language models multimodal multimodal ai transformer architecture Authors Affiliations William Jack 0009-0001-4899-8362 [email protected] Department of Computer Science, University of California View all articles by this author Metrics & Citations Metrics Article Usage 341 views 168 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation William Jack. Beyond Words: The Evolution of Large Language Models in Context-Aware. Authorea . 03 March 2025. DOI: https://doi.org/10.22541/au.174102439.94682577/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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