Performance Evaluation of Large Language Models: A Comprehensive Review

Abstract

The performance evaluation techniques for large language models (LLMs) are thoroughly reviewed in this study. As these models' capabilities and applications continue to develop quickly, establishing robust evaluation frameworks becomes increasingly important. We analyze the evolution of evaluation metrics and benchmarks, from traditional natural language processing assessments to more recent LLM-specific frameworks. The paper includes detailed performance comparisons across leading models including GPT-4, Claude 3, LLaMA 2, Gemini, and PaLM 2 on established benchmarks. We identify key challenges in current evaluation practices, including reliability concerns, emergent capability assessment, and alignment evaluation. Additionally, we examine the multi-dimensional nature of LLM evaluation, encompassing not only task performance but also safety, efficiency, fairness, and environmental impact considerations. The review concludes with recommendations for developing more holistic evaluation methodologies and identifies interesting avenues for further study in this quickly developing discipline.