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    <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.</abstract>
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%0 Conference Proceedings
%T Multi-Document Event Extraction Using Large and Small Language Models
%A Min, Qingkai
%A Qu, Zitian
%A Guo, Qipeng
%A Hu, Xiangkun
%A Zhang, Zheng
%A Zhang, Yue
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F min-etal-2025-multi
%X 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.
%U https://aclanthology.org/2025.emnlp-main.972/
%P 19265-19296

Markdown (Informal)

[Multi-Document Event Extraction Using Large and Small Language Models](https://aclanthology.org/2025.emnlp-main.972/) (Min et al., EMNLP 2025)

• Multi-Document Event Extraction Using Large and Small Language Models (Min et al., EMNLP 2025)

ACL

• Qingkai Min, Zitian Qu, Qipeng Guo, Xiangkun Hu, Zheng Zhang, and Yue Zhang. 2025. Multi-Document Event Extraction Using Large and Small Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 19265–19296, Suzhou, China. Association for Computational Linguistics.