Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation
Authors: name: Xue Liu, name: Xin Ma, name: Yuxin Ma, name: Yongchang Peng, name: Duo Wang, name: Zhoufutu Wen, name: Ge Zhang, name: Kaiyuan Zhang, name: Xinyu Chen, name: Tianci He, name: Jiani Hou, name: Liang Hu, name: Ziyun Huang, name: Yongzhe Hui, name: Jianpeng Jiao, name: Chennan Ju, name: Yingru Kong, name: Yiran Li, name: Mengyun Liu, name: Luyao Ma, name: Fei Ni, name: Yiqing Ni, name: Yueyan Qiu, name: Yanle Ren, name: Zilin Shi, name: Zaiyuan Wang, name: Wenjie Yue, name: Shiyu Zhang, name: Xinyi Zhang, name: Kaiwen Zhao, name: Zhenwei Zhu

As Large Language Models (LLMs) exhibit plateauing performance on conventional benchmarks, a pivotal challenge persists: evaluating their proficiency in complex, open-ended tasks characterizing genuine expert-level cognition. Existing frameworks suffer from narrow domain coverage, reliance on generalist tasks, or self-evaluation biases. To bridge this gap, we present XpertBench, a high-fidelity benchmark engineered to assess LLMs across authentic professional domains. XpertBench consists of 1,346 meticulously curated tasks across 80 categories, spanning finance, healthcare, legal services, education, and dual-track research (STEM and Humanities). These tasks are derived from over 1,000 submissions by domain experts--including researchers from elite institutions and practitioners with extensive clinical or industrial experience--ensuring superior ecological validity. Each task uses detailed rubrics with mostly 15-40 weighted checkpoints to assess professional rigor. To facilitate scalable yet human-aligned assessment, we introduce ShotJudge, a novel evaluation paradigm that employs LLM judges calibrated with expert few-shot exemplars to mitigate self-rewarding biases. Our empirical evaluation of state-of-the-art LLMs reveals a pronounced performance ceiling: even leading models achieve a peak success rate of only ~66%, with a mean score around 55%. Models also exhibit domain-specific divergence, showing non-overlapping strengths in quantitative reasoning versus linguistic synthesis.. These findings underscore a significant "expert-gap" in current AI systems and establish XpertBench as a critical instrument for navigating the transition from general-purpose assistants to specialized professional collaborators.