SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

M-A-P Team, du, Yifan Yao, Kaijing Ma, Bingli Wang, TY.Zheng, king zhu, minghao, yimingliang, Xiaolong Jin, Zela, Chujie Zheng, Kaixing Deng, Shuyue Guo, Shian Jia, jiangsichao, Yiyan Liao, Rui Li, Qinrui Li, Sirun Li, Yizhi Li, Yunwen Li, Dehua Ma, Yuansheng Ni, Haoran Que, Mathsion Wong, Maxwell Wen, SiweiWu, xts, Ming Xu, Zhenzhu Yang, Zekun Moore Wang, Junting Zhou, Yuelin Bai, Xingyuan Bu, caichenglin, Liang Chen, Yifan Chen, Chengtuo Cheng, Tianhao Cheng, Keyi Ding, Siming Huang, Yuna Huang, Yaoru Li, Yizhe Li, Zhaoqun Li, Tianhao Liang, lin, Hongquan Lin, Yinghao Ma, zhongyuan peng, PENG ZIFAN, Qige Qi, SHI QIU, Xingwei Qu, Yizhou Tan, Zili Wang, Chenqing Wang, Hao Wang, Yiya Wang, Yubo Wang, Jiajun Xu, Kexin Yang, Ruibin Yuan, Yuanhao Yue, Tianyang Zhan, Chun Zhang, Jingyang Zhang, Xiyue Zhang, Xingjian Zhang, Yue Zhang, Yongchi Zhao, Xiangyu Zheng, Chenghua Zhong, Yang Gao, Zhoujun Li, Dayiheng Liu, Qian Liu, Tianyu Liu, Shiwen Ni, Junran Peng, Yujia Qin, Wenbo Su, Guoyin Wang, Shi Wang, Jian Yang, Min Yang, Meng Cao, Xiang Yue, Zhaoxiang Zhang, Zhou, Jiaheng Liu, Qunshu Lin, Wenhao Huang, Ge Zhang

SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines: 110 upvotes on Hugging Face Daily Papers, #3 of 29 papers on 2025-02-21. Day-by-day upvote history.

Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledge encompasses over 200 specialized disciplines, far exceeding the scope of existing benchmarks. The capabilities of LLMs in many of these specialized fields-particularly in light industry, agriculture, and service-oriented disciplines-remain inadequately evaluated. To address this gap, we present SuperGPQA, a comprehensive benchmark that evaluates graduate-level knowledge and reasoning capabilities across 285 disciplines. Our benchmark employs a novel Human-LLM collaborative filtering mechanism to eliminate trivial or ambiguous questions through iterative refinement based on both LLM responses and expert feedback. Our experimental results reveal significant room for improvement in the performance of current state-of-the-art LLMs across diverse knowledge domains (e.g., the reasoning-focused model DeepSeek-R1 achieved the highest accuracy of 61.82% on SuperGPQA), highlighting the considerable gap between current model capabilities and artificial general intelligence. Additionally, we present comprehensive insights from our management of a large-scale annotation process, involving over 80 expert annotators and an interactive Human-LLM collaborative system, offering valuable methodological guidance for future research initiatives of comparable scope.

Paper page on Hugging Face · arXiv

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