Efficiency in Language Understanding and Generation: An Evaluation of Four Open-Source Large Language Models

Abstract This study provides a comprehensive evaluation of the efficiency of Large Language Models (LLMs) in performing diverse language understanding and generation tasks. Through a systematic comparison of open-source models including GPT-Neo, Bloom, FLAN-T5, and Mistral-7B, the research explores their performance across widely recognized benchmarks such as GLUE, SuperGLUE, LAMBADA, and SQuAD. Our findings reveal significant variations in model accuracy, computational efficiency, scalability, and adaptability, underscoring the influence of model architecture and training paradigms on performance outcomes. The study identifies key factors contributing to the models' efficiency and offers insights into potential optimization strategies for enhancing their applicability in real-world NLP applications. By highlighting the strengths and limitations of current LLMs, this research contributes to the ongoing development of more effective, efficient, and adaptable language models, paving the way for future advancements in the field of natural language processing.