Large Language Models in Machine Learning: Architectures, Applications, and Ethical Challenges

Author(s)	Prof. POORNIMA CHOURASIA, Prof. Dr. YOGENDRA SINGH RAJAVAT
Country	India
Abstract	Large Language Models (LLMs) have redefined the boundaries of machine learning by enabling machines to understand, generate, and reason with human language at scale. Built upon transformer architectures, LLMs such as GPT, BERT, and LLaMA have demonstrated significant breakthroughs in natural language processing tasks, including translation, summarization, question answering, and even programming. This paper reviews the architectural foundations of LLMs, evaluates their practical applications across disciplines, and explores fine-tuning techniques such as few-shot learning and reinforcement learning from human feedback (RLHF). While LLMs show immense promise, they also introduce serious concerns related to bias, explainability, environmental cost, and misinformation. We conclude by highlighting future research directions in multimodal models, efficiency optimization, and ethical deployment strategies.
Keywords	Large Language Models, Machine Learning, Transformers, NLP, GPT, BERT, AI Ethics, RLHF, Multimodal AI
Field	Computer Applications
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-21
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.45520
Short DOI	https://doi.org/g9mh8r

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research