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）因其在各类应用中展现出的前所未有的性能，在学术界和工业界日益受到青睐。随着LLMs在研究与日常使用中持续发挥关键作用，对其评估变得愈发重要——这不仅体现在任务层面，更体现在社会层面，以便更好地理解其潜在风险。过去数年间，研究者们已从多角度对LLMs进行了大量考察。本文系统综述了这些针对LLMs的评估方法，聚焦于三个核心维度：评估内容、评估场景与评估方式。首先，我们从评估任务视角展开综述，涵盖通用自然语言处理任务、推理能力、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与基准（评估LLM性能的关键组成部分），我们回答了"评估场景"与"评估方式"的问题。接着，我们总结了LLMs在不同任务中的成功案例与失败案例。最后，我们揭示了LLMs评估领域面临的若干未来挑战。本文旨在为LLMs评估研究者提供宝贵见解，从而助力开发更高效的LLMs。我们的核心观点是：应将评估作为一门基础学科，以更好地推动LLMs的发展。相关开源资料持续维护于：https://github.com/MLGroupJLU/LLM-eval-survey。