@inproceedings{min-etal-2025-multi,
    title = "Multi-Document Event Extraction Using Large and Small Language Models",
    author = "Min, Qingkai  and
      Qu, Zitian  and
      Guo, Qipeng  and
      Hu, Xiangkun  and
      Zhang, Zheng  and
      Zhang, Yue",
    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.972/",
    pages = "19265--19296",
    ISBN = "979-8-89176-332-6",
    abstract = "Multi-document event extraction aims to aggregate event information from diverse sources for a comprehensive understanding of complex events. Despite its practical significance, this task has received limited attention in existing research. The inherent challenges include handling complex reasoning over long contexts and intricate event structures. In this paper, we propose a novel collaborative framework that integrates large language models for multi-step reasoning and fine-tuned small language models to handle key subtasks, guiding the overall reasoning process. We introduce a new benchmark for multi-document event extraction and propose an evaluation metric designed for comprehensive assessment of multiple aggregated events. Experimental results demonstrate that our approach significantly outperforms existing methods, providing new insights into collaborative reasoning to tackle the complexities of multi-document event extraction."
}