A Survey on Evaluation of Large Language Models
Authors: name: Yupeng Chang, name: Xu Wang, name: Jindong Wang, name: Yuan Wu, name: Kaijie Zhu, name: Hao Chen, name: Linyi Yang, name: Xiaoyuan Yi, name: Cunxiang Wang, name: Yidong Wang, name: Wei Ye, name: Yue Zhang, name: Yi Chang, name: Philip S. Yu, name: Qiang Yang, name: Xing Xie

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.