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Large Language Models for NLP: An In-Depth Comparative Examination
Zakaria Alomari (New York Institute of Technology, New York Institute of Technology), Ojasvi Sharma (New York Institute of Technology, New York Institute of Technology), Shivam Sawarn, Yunlong Shao (New York Institute of Technology, New York Institute of Technology), Adetokunbo Makanju (New York Institute of Technology, New York Institute of Technology)
Abstract: The discipline of Large Language Models (LLMs) is rapidly advancing, and it is essential to explore their capabilities and limitations for further development. This study conducts a comparative analysis of six prominent models: GPT-4, LLaMA 2, FALCON, BLOOM, LaMDA and DeepSeek. By evaluating their performance across a range of tasks and benchmarks, including model size, training methodologies, and unique functionalities, we aim to identify the current state-of-the-art and propose directions for future research. Through this examination, we seek to enhance the understanding of LLMs and contribute to their responsible and efficient deployment.