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The Fine-Tuning Journey of ChatGPT in Industry Applications: Domains From Generic to Specific

The Fine-Tuning Journey of ChatGPT in Industry Applications: Domains From Generic to Specific
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Author(s): Hitesh Mohapatra (School of Computer Engineering, KIIT University, Bhubaneswar, India)
Copyright: 2025
Pages: 22
Source title: Humans and Generative AI Tools for Collaborative Intelligence
Source Author(s)/Editor(s): Jingyuan Zhao (University of Toronto, Canada), V. Vinoth Kumar (Vellore Institute of Technology, India), Polinpapilinho F. Katina (University of South Carolina Upstate, USA)and Joseph Richards (California State University, Sacramento, USA)
DOI: 10.4018/979-8-3693-8332-2.ch011

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Abstract

The recent evolution of natural language processing (NLP) has introduced models like ChatGPT, capable of handling various language-related tasks. However, generic models like ChatGPT may lack accuracy in specialized fields such as healthcare or finance. Fine-tuning these models enables adaptation to specific industry domains, improving performance and relevance. This paper delves into the concept of fine-tuning ChatGPT for specialized domains, examining the technical challenges and solutions involved in the process. We also discuss real-world applications across healthcare, financial services, and education, highlighting the benefits and ethical considerations. The paper concludes with future trends in fine-tuning techniques for broader applicability.

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