Papers by Qiyuan Zheng
RubricBench: Aligning Model-Generated Rubrics with Human Standards (2026.acl-long)
Copied to clipboard
Junyi Zhou, Qiyuan Zhang, Yufei Wang, Fuyuan Lyu, Yidong Ming, Can Xu, Qingfeng Sun, Kai Zheng, Peng Kang, Xue Liu, Chen Ma
| Challenge: | Existing benchmarks lack discriminative complexity and ground-truth rubric annotations required for rigorous evaluation. |
| Approach: | They propose a curated benchmark with 1,147 pairwise comparisons to assess the reliability of rubric-based evaluation. |
| Outcome: | The proposed benchmarks show that they support diverse domains, exhibit discriminative ability, provide high-quality annotations, and include human-authored rubrics. |
Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in Generative Reward Models have demonstrated that scaling the length of Chain-of-Thought reasoning enhances reliability of evaluation. |
| Approach: | They propose a framework that reconfigures raw rationales into structured Breadth-CoT and Depth-Co T through a modular synthesis pipeline. |
| Outcome: | The proposed framework surpasses open-source RMs by an average of 8.2%. |
Taming Language Models for Text-attributed Graph Learning with Decoupled Aggregation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to learning text-attributed graphs neglect interaction between textual and structural information. |
| Approach: | They propose a framework that integrates textual and structural information into TAG learning . they propose combining semantic aggregation and structural aggregations to improve learning a . |
| Outcome: | The proposed framework outperforms state-of-the-art learning methods while requiring less resources. |
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)
Copied to clipboard
Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction. |
| Approach: | They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack. |
| Outcome: | The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. |