@inproceedings{liu-etal-2025-towards-event,
    title = "Towards Event Extraction with Massive Types: {LLM}-based Collaborative Annotation and Partitioning Extraction",
    author = "Liu, Wenxuan  and
      Li, Zixuan  and
      Bai, Long  and
      Zuo, Yuxin  and
      Xu, Daozhu  and
      Jin, Xiaolong  and
      Guo, Jiafeng  and
      Cheng, Xueqi",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1743/",
    pages = "34365--34387",
    ISBN = "979-8-89176-332-6",
    abstract = "Developing a general-purpose system that can extract events with massive types is a long-standing target in Event Extraction (EE). In doing so, the basic challenge comes from the absence of an efficient and effective annotation framework to construct the corresponding datasets. In this paper, we propose an LLM-based collaborative annotation framework. Through collaboration among multiple LLMs and a subsequent voting process, it refines annotations of triggers from distant supervision and then carries out argument annotation. Finally, we create EEMT, the largest EE dataset to date, featuring over **200,000** samples, **3,465** event types, and **6,297** role types. Evaluation on human-annotated test set demonstrates that the proposed framework achieves the F1 scores of **90.1{\%}** and **85.3{\%}** for event detection and argument extraction, strongly validating its effectiveness. Besides, to alleviate the excessively long prompts caused by massive types, we propose an LLM-based Partitioning method for EE called LLM-PEE. It first recalls candidate event types and then splits them into multiple partitions for LLMs to extract. After fine-tuning on the EEMT training set, the distilled LLM-PEE with 7B parameters outperforms state-of-the-art methods by **5.4{\%}** and **6.1{\%}** in event detection and argument extraction. Besides, it also surpasses mainstream LLMs by **12.9{\%}** on the unseen datasets, which strongly demonstrates the event diversity of the EEMT dataset and the generalization capabilities of the LLM-PEE method."
}