Efficiency in Language Understanding and Generation: An Evaluation of Four Open-Source Large Language Models

Efficiency in Language Understanding and Generation: An Evaluation of Four Open-Source Large Language Models | 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 Efficiency in Language Understanding and Generation: An Evaluation of Four Open-Source Large Language Models Siu Ming Wong, Ho Leung, Ka Yan Wong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4063228/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 This study provides a comprehensive evaluation of the efficiency of Large Language Models (LLMs) in performing diverse language understanding and generation tasks. Through a systematic comparison of open-source models including GPT-Neo, Bloom, FLAN-T5, and Mistral-7B, the research explores their performance across widely recognized benchmarks such as GLUE, SuperGLUE, LAMBADA, and SQuAD. Our findings reveal significant variations in model accuracy, computational efficiency, scalability, and adaptability, underscoring the influence of model architecture and training paradigms on performance outcomes. The study identifies key factors contributing to the models' efficiency and offers insights into potential optimization strategies for enhancing their applicability in real-world NLP applications. By highlighting the strengths and limitations of current LLMs, this research contributes to the ongoing development of more effective, efficient, and adaptable language models, paving the way for future advancements in the field of natural language processing. Artificial Intelligence and Machine Learning Large Language Models Natural Language Processing Model Efficiency Computational Efficiency Scalability Adaptability 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. 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