文献类型：期刊文献

英文题名：Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation

作者：Gu, Zhouhong[1]; Zhu, Xiaoxuan[1]; Ye, Haoning[1]; Zhang, Lin[1]; Wang, Jianchen[1]; Zhu, Yixin[1]; Jiang, Sihang[1]; Xiong, Zhuozhi[1]; Li, Zihan[1]; Wu, Weijie[1]; He, Qianyu[1]; Xu, Rui[1]; Huang, Wenhao[1]; Liu, Jingping[2]; Wang, Zili[5]; Wang, Shusen[5]; Zheng, Weiguo[3]; Feng, Hongwei[1]; Xiao, Yanghua[1,4,5]

机构：[1] Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, China; [2] School of Information Science and Engineering, East China University of Science and Technology, China; [3] School of Data Science, Fudan University, China; [4] Fudan-Aishu Cognitive Intelligence Joint Research Center, China; [5] Research Group of Computational and AI Communication, Institute for Global Communications and Integrated Media, Fudan University, China

年份：2023

外文期刊名：arXiv

收录：EI(收录号：20230221119)

语种：英文

摘要：New Natural Langauge Process (NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present Xiezhi, the most comprehensive evaluation suite designed to assess holistic domain knowledge. Xiezhi comprises multiple-choice questions across 516 diverse disciplines ranging from 13 different subjects with 249,587 questions and accompanied by Xiezhi-Specialty with 14,041 questions and Xiezhi-Interdiscipline with 10,746 questions. We conduct evaluation of the 47 cutting-edge LLMs on Xiezhi. Results indicate that LLMs exceed average performance of humans in science, engineering, agronomy, medicine, and art, but fall short in economics, jurisprudence, pedagogy, literature, history, and management. ? 2023, CC BY.