StatEval: A Comprehensive Benchmark for Large Language Models in Statistics
Authors
Yuchen Lu
Run Yang
Yichen Zhang
Shuguang Yu
Runpeng Dai
Ziwei Wang
Jiayi Xiang
Wenxin E
Siran Gao
Xinyao Ruan
Yirui Huang
Chenjing Xi
Haibo Hu
Yueming Fu
Qinglan Yu
Xiaobing Wei
Jiani Gu
Rui Sun
Jiaxuan Jia
Fan Zhou
Abstract
Large language models (LLMs) have demonstrated remarkable advances in mathematical and logical reasoning, yet statistics, as a distinct and integrative discipline, remains underexplored in benchmarking efforts. To address this gap, we introduce \textbf{StatEval}, the first comprehensive benchmark dedicated to statistics, spanning both breadth and depth across difficulty levels. StatEval consists of 13,817 foundational problems covering undergraduate and graduate curricula, together with 2374 research-level proof tasks extracted from leading journals. To construct the benchmark, we design a scalable multi-agent pipeline with human-in-the-loop validation that automates large-scale problem extraction, rewriting, and quality control, while ensuring academic rigor. We further propose a robust evaluation framework tailored to both computational and proof-based tasks, enabling fine-grained assessment of reasoning ability. Experimental results reveal that while closed-source models such as GPT5-mini achieve below 57\% on research-level problems, with open-source models performing significantly lower. These findings highlight the unique challenges of statistical reasoning and the limitations of current LLMs. We expect StatEval to serve as a rigorous benchmark for advancing statistical intelligence in large language models. All data and code are available on our web platform: https://stateval.github.io/.
