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.

Cross-document event coreference resolution (CDECR) involves clustering event mentions across multiple documents that refer to the same real-world events.Existing approaches utilize fine-tuning of small language models (SLMs) like BERT to address the compatibility among the contexts of event mentions.However, due to the complexity and diversity of contexts, these models are prone to learning simple co-occurrences.Recently, large language models (LLMs) like ChatGPT have demonstrated impressive contextual understanding, yet they encounter challenges in adapting to specific information extraction (IE) tasks.In this paper, we propose a collaborative approach for CDECR, leveraging the capabilities of both a universally capable LLM and a task-specific SLM.The collaborative strategy begins with the LLM accurately and comprehensively summarizing events through prompting.Then, the SLM refines its learning of event representations based on these insights during fine-tuning.Experimental results demonstrate that our approach surpasses the performance of both the large and small language models individually, forming a complementary advantage.Across various datasets, our approach achieves stateof-the-art performance, underscoring its effectiveness in diverse scenarios.

Bowen Ding, Qingkai Min, Shengkun Ma, Yingjie Li, Linyi Yang, Yue Zhang. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Dialogue state modules are a useful component in a task-oriented dialogue system. Traditional methods find dialogue states by manually labeling training corpora, upon which neural models are trained. However, the labeling process can be costly, slow, error-prone, and more importantly, cannot cover the vast range of domains in real-world dialogues for customer service. We propose the task of dialogue state induction, building two neural latent variable models that mine dialogue states automatically from unlabeled customer service dialogue records. Results show that the models can effectively find meaningful dialogue states. In addition, equipped with induced dialogue states, a state-of-the-art dialogue system gives better performance compared with not using a dialogue state module.

Qingkai Min, Yuefeng Shi, Yue Zhang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.