@inproceedings{novak-etal-2025-findings,
title = "Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can {LLM}s Dethrone Traditional Approaches?",
author = "Nov{\'a}k, Michal and
Konopik, Miloslav and
Nedoluzhko, Anna and
Popel, Martin and
Prazak, Ondrej and
Sido, Jakub and
Straka, Milan and
{\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k and
Zeman, Daniel",
editor = "Ogrodniczuk, Maciej and
Novak, Michal and
Poesio, Massimo and
Pradhan, Sameer and
Ng, Vincent",
booktitle = "Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.crac-1.9/",
pages = "95--118",
abstract = "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."
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%0 Conference Proceedings
%T Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can LLMs Dethrone Traditional Approaches?
%A Novák, Michal
%A Konopik, Miloslav
%A Nedoluzhko, Anna
%A Popel, Martin
%A Prazak, Ondrej
%A Sido, Jakub
%A Straka, Milan
%A Žabokrtský, Zdeněk
%A Zeman, Daniel
%Y Ogrodniczuk, Maciej
%Y Novak, Michal
%Y Poesio, Massimo
%Y Pradhan, Sameer
%Y Ng, Vincent
%S Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%F novak-etal-2025-findings
%X 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.
%U https://aclanthology.org/2025.crac-1.9/
%P 95-118
Markdown (Informal)
[Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can LLMs Dethrone Traditional Approaches?](https://aclanthology.org/2025.crac-1.9/) (Novák et al., CRAC 2025)
ACL
- Michal Novák, Miloslav Konopik, Anna Nedoluzhko, Martin Popel, Ondrej Prazak, Jakub Sido, Milan Straka, Zdeněk Žabokrtský, and Daniel Zeman. 2025. Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can LLMs Dethrone Traditional Approaches?. In Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference, pages 95–118, Suzhou, China. Association for Computational Linguistics.