Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, educations, natural and social sciences, agent applications, and other areas. Secondly, we answer the `where' and `how' questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs. Our key point is that evaluation should be treated as an essential discipline to better assist the development of LLMs. We consistently maintain the related open-source materials at: https://github.com/MLGroupJLU/LLM-eval-survey.

翻译：大语言模型因其在各类应用中表现出的卓越性能，在学术界和工业界日益受到关注。随着大语言模型在研究和日常生活中持续发挥关键作用，其评估体系正变得愈发重要——不仅需关注任务层级表现，更要深入理解其社会层面的潜在风险。过去数年间，研究者已从多个维度对大语言模型展开系统性考察。本文针对这些评估方法进行了全面综述，聚焦三个关键维度：评估内容、评估场景与评估方法。首先，我们从评估任务视角进行概述，涵盖通用自然语言处理任务、推理能力、医疗应用、伦理考量、教育领域、自然科学与社会科学、智能体应用及其他方向。其次，通过深入剖析作为评估关键组件的评估方法与基准数据集，系统解答了"何处评估"与"如何评估"两大核心问题。继而，我们归纳了大语言模型在不同任务中的成功案例与失败案例。最后，本文揭示了大语言模型评估领域面临的若干未来挑战。本研究旨在为大语言模型评估领域的研究者提供宝贵见解，从而助力更优质大语言模型的开发。我们的核心观点是：评估应当作为一门基础性学科，以更好地支撑大语言模型的发展。相关开源资料持续维护于：https://github.com/MLGroupJLU/LLM-eval-survey。