Factors to Consider When Selecting a Large Language Model: A Comparative Analysis

Abstract

As organizations strive to integrate these models into their systems, the pivotal challenge they face is selecting the most appropriate alternative. The task of selecting an appropriate LLM for organizational integration remains complex. This paper presents a comprehensive analysis of factors that should be considered when choosing a LLM, aiming to align the selected model with specific organizational goals. The comparison involves five prominent LLMs: ChatGPT, Bard, Lamma, Hugging Face, and GitHub Copilot. The findings highlight the significance of certain factors in LLM selection. Pre-training data diversity, as observed in ChatGPT and Bard, enhances language coverage and response accuracy. Larger models, like ChatGPT and Bard, exhibit superior comprehension and logical responses due to their extensive parameter count. Training time considerations are crucial, with models such as Bard and Lamma requiring months for training, while Hugging Face and GitHub Copilot offer faster training periods. Language support emerges as a key determinant based on organizational needs. Models like Lamma focus on scientific language, while ChatGPT and Bard emphasize broad language coverage. Enterprise readiness, user data control, and real-time research capabilities are pivotal in decision-making. The study also reveals distinctions in model purposes, API capabilities, user feedback mechanisms, and cloud provider support.