Event Coreference Resolution (ECR) as a pairwise mention classification task
is expensive both for automated systems and manual annotations. The task's
quadratic difficulty is exacerbated when using Large Language Models (LLMs),
making prompt engineering for ECR prohibitively costly. In this work, we
propose a graphical representation of events, X-AMR, anchored around individual
mentions using a \textbf{cross}-document version of \textbf{A}bstract
\textbf{M}eaning \textbf{R}epresentation. We then linearize the ECR with a
novel multi-hop coreference algorithm over the event graphs. The event graphs
simplify ECR, making it a) LLM cost-effective, b) compositional and
interpretable, and c) easily annotated. For a fair assessment, we first enrich
an existing ECR benchmark dataset with these event graphs using an
annotator-friendly tool we introduce. Then, we employ GPT-4, the newest LLM by
OpenAI, for these annotations. Finally, using the ECR algorithm, we assess
GPT-4 against humans and analyze its limitations. Through this research, we aim
to advance the state-of-the-art for efficient ECR and shed light on the
potential shortcomings of current LLMs at this task. Code and annotations:
https://github.com/ahmeshaf/gpt_coref

本研究通过引入事件图的图形化表示 X-AMR 以及使用一种新型的多跳共指算法简化了事件共指消解，从而使其成为 LLM 成本效益高、可组合和可解释的，并且易于注释，进一步使用 GPT-4 与人类进行比较评估，分析了其局限性，旨在推进有效的事件共指消解技术并揭示当前 LLM 在此任务中的潜在缺陷。