@inproceedings{zhu-etal-2025-towards,
    title = "Towards Robust Universal Information Extraction: Dataset, Evaluation, and Solution",
    author = "Zhu, Jizhao  and
      Shi, Akang  and
      Li, Zixuan  and
      Bai, Long  and
      Jin, Xiaolong  and
      Guo, Jiafeng  and
      Cheng, Xueqi",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1360/",
    pages = "28052--28070",
    ISBN = "979-8-89176-251-0",
    abstract = "In this paper, we aim to enhance the robustness of Universal Information Extraction (UIE) by introducing a new benchmark dataset, a comprehensive evaluation, and a feasible solution. Existing robust benchmark datasets have two key limitations: 1) They generate only a limited range of perturbations for a single Information Extraction (IE) task, which fails to evaluate the robustness of UIE models effectively; 2) They rely on small models or handcrafted rules to generate perturbations, often resulting in unnatural adversarial examples. Considering the powerful generation capabilities of Large Language Models (LLMs), we introduce a new benchmark dataset for Robust UIE, called RUIE-Bench, which utilizes LLMs to generate more diverse and realistic perturbations across different IE tasks. Based on this dataset, we comprehensively evaluate existing UIE models and reveal that both LLM-based models and other models suffer from significant performance drops. To improve robustness and reduce training costs, we propose a data-augmentation solution that dynamically selects hard samples for iterative training based on the model{'}s inference loss. Experimental results show that training with only $\textbf{15}${\%} of the data leads to an average $\textbf{8.1}${\%} relative performance improvement across three IE tasks. Our code and dataset are available at: https://github.com/ICT-GoKnow/RobustUIE."
}