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：大型语言模型（LLMs）在学术界和工业界中的应用日益广泛，其在各类任务中展现出的前所未有的性能使其备受关注。随着LLMs在研究和日常使用中扮演越来越重要的角色，对其评估变得尤为关键——不仅需要在任务层面进行，更需在社会层面深入理解其潜在风险。近年来，研究人员已从多角度对LLMs展开系统考察。本文对LLMs评估方法进行全面综述，聚焦三个核心维度：评估什么、在哪里评估以及如何评估。首先，我们从评估任务视角出发，涵盖通用自然语言处理任务、推理、医疗应用、伦理、教育、自然科学与社会科学、智能体应用及其他领域。其次，通过深入分析评估方法和基准（作为衡量LLMs性能的关键要素），我们对"在哪里评估"与"如何评估"的问题进行解答。随后，我们总结了LLMs在不同任务中的成功案例与失败案例。最后，我们展望了LLMs评估领域面临的若干未来挑战。本文旨在为LLMs评估研究人员提供宝贵洞见，从而助力开发更强大的LLMs。我们的核心理念是：评估应被视作一门基础性学科，以更好推动LLMs的发展。相关开源资源持续维护于：https://github.com/MLGroupJLU/LLM-eval-survey。