ItemBook Chapter

Impact of fine-tuning large language model in society: a comprehensive study

Title
Impact of fine-tuning large language model in society: a comprehensive study
Creator
Chandra, J.; Joseph, Alwin; Joseph, Joel; Upadhyay, Priyansha; Kumar, Satyam
Description
The fine-tuned large language models (LLMs) have revolutionized artificial intelligence (AI) and natural language processing (NLP) with key innovations in neural architectures, particularly with the transformer. The recent advancements of LLM have witnessed that models like bidirectional encoder representations from transformer (BERT), generative pretrained transformer (GPT)-2, GPT-3, and text-to-text transfer transformer (T5) show outstanding performance in understanding and generating human-like text at scale. Researchers use fine-tuned models to excel in their responses to specific tasks or domains. The purpose of fine-tuning the LLM models is to improve the performance of LLM in special fields such as education, research, literature summarization, contract analysis, and creative content generation. Fine-tuning LLM models also has issues like amplifying biases, ethical issues, and regulatory implications, remarkably when LLMs are fine-tuned for emerging domains that may hold harmful stereotypes or misinformation. Fine-tuned LLMs also provide substantial societal benefits, including expert-level knowledge to underserved regions and personalizing educational resources for self-directed learning. The study also discusses the technical aspects of fine-tuning LLMs by examining how general-purpose models are transformed into efficient models. The impact on society and the need for a framework that can shape the deployment of models, with ethical guardrails, transparency, and public engagement to ensure responsible development and use of fine-tuned LLMs. The current work explores the various steps that can be taken for bias mitigation and transparent documentation for different stakeholder engagements. The purpose of the chapter is to analyse the perspectives from technical foundations with ethical, cultural, and policy considerations and provides an integral view of the societal impact of fine-tuned LLMs. 2026 Elsevier Inc. All rights reserved.
Source
Challenges and Applications of Generative Large Language Models;pp.23-45
Date
01-01-2026
Publisher
Elsevier
Subject
artificial intelligence; computing; information management; information retrieval; knowledge management; Machine learning; natural language processing; transformers
Coverage
Chandra J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Joseph A., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Joseph J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Upadhyay P., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Kumar S., Department of Computer Science, Christ University, Karnataka, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-044333592-1; 978-044333593-8;
Format
online
Language
English
Type
Book chapter

Citation

Chandra, J.; Joseph, Alwin; Joseph, Joel; Upadhyay, Priyansha; Kumar, Satyam, “Impact of fine-tuning large language model in society: a comprehensive study,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/24233.

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