Authors

Abstract

The rapid advancements in Large Language Models (LLMs,) such as OpenAI’s GPT, Meta’s LLaMA, and Google’s PaLM, have revolutionized natural language processing and various AI-driven applications. Despite their transformative impact, a standardized metric to compare these models poses a significant challenge for researchers and practitioners. This paper addresses the urgent need for a comprehensive evaluation framework by proposing a novel performance ranking metric. Our metric integrates both qualitative and quantitative assessments to provide a holistic comparison of LLM capabilities. Through rigorous benchmarking, we analyze the strengths and limitations of leading LLMs, offering valuable insights into their relative performance. This study aims to facilitate informed decision-making in model selection and promote advances in developing more robust and efficient language models.

Keywords

Large Language Models (LLMs), Performance Evaluation, and Benchmarking.