  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 state-of-the-art
performance, underscoring its effectiveness in diverse scenarios.
