A Comprehensive Evaluation of Llama 3 for Text Classification Tasks

A Comprehensive Evaluation of Llama 3 for Text Classification Tasks | 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 Article A Comprehensive Evaluation of Llama 3 for Text Classification Tasks AmirAhmad Amjadi, Shiva TaghipourEivazi, Bahman Arasteh, Huseyin Kusetogullari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8354244/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 Text classification (TC) is one of core element of natural language processing (NLP), becoming more important as volume of text data increases. It is widely used in applications such as sentiment analysis, and topic categorization. Large language models (LLMs) have recently shown strong performance in these tasks, even without task-specific training. In this study, we evaluate Llama 3 8B model for text classification using zero-shot, and few-shot methods across many datasets. This study evaluates Llama 3 8B results on different NLP downstream tasks. Our findings show that Llama 3 8B can perform competitively across different settings, especially when guided by well-designed prompts. This study highlights how decoder LLMs can be effectively applied to classification tasks and provides acceptable performance. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Physical sciences/Mathematics and computing Text Classification PLM LLM Llama BERT GPT Full Text Additional Declarations No competing interests reported. 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. 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-8354244","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":563082587,"identity":"dd74321a-3be1-4ebf-84f2-35e458bfe847","order_by":0,"name":"AmirAhmad Amjadi","email":"","orcid":"","institution":"Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"AmirAhmad","middleName":"","lastName":"Amjadi","suffix":""},{"id":563082588,"identity":"516252d0-54c1-4033-88ae-4ab109bde4ad","order_by":1,"name":"Shiva 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