LLM在多语言共指消解中展现潜力，或可挑战传统方法。
Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can LLMs Dethrone Traditional Approaches?
- 设立LLM专用赛道，采用更适配大模型的纯文本格式。
- 九个系统参赛，传统方法仍领先，但LLM表现明显优于往年。
- 适合关注多语言共指与LLM应用的研究者参考。
本文综述了2025年CODI-CRAC研讨会举办的第四届多语言共指消解共享任务。参赛者需识别提及并按共指关系聚类。本年度任务引入专为大语言模型（LLM）设计的赛道，采用简化版纯文本格式，优于原有的CoNLL-U表示。任务覆盖范围扩展至新增的三组数据集，涵盖两种新语言，基于CorefUD v1.3——一个包含22个数据集、17种语言的标准化多语言语料库。共有九个系统参与，包括四个基于LLM的方法（两个微调，两个少样本适配）。尽管传统方法仍保持领先，但LLM展现出显著进步，预示其未来可能挑战现有主流方法。
原文摘要 · Abstract (English)
The paper presents an overview of the fourth edition of the Shared Task on Multilingual Coreference Resolution, organized as part of the CODI-CRAC 2025 workshop. As in the previous editions, participants were challenged to develop systems that identify mentions and cluster them according to identity coreference. A key innovation of this year's task was the introduction of a dedicated Large Language Model (LLM) track, featuring a simplified plaintext format designed to be more suitable for LLMs than the original CoNLL-U representation. The task also expanded its coverage with three new datasets in two additional languages, using version 1.3 of CorefUD - a harmonized multilingual collection of 22 datasets in 17 languages. In total, nine systems participated, including four LLM-based approaches (two fine-tuned and two using few-shot adaptation). While traditional systems still kept the lead, LLMs showed clear potential, suggesting they may soon challenge established approaches in future editions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。