# Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can LLMs Dethrone Traditional Approaches?

arXiv: https://arxiv.org/abs/2509.17796 · HTML: https://mlsanity.com/paper/2509.17796/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 54 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [An Annotated Dataset of Coreference in English Literature](https://mlsanity.com/paper/1912.01140/) (2019)
- [Word-Level Coreference Resolution](https://mlsanity.com/paper/2109.04127/) (2021)
- [Are Large Language Models Robust Coreference Resolvers?](https://mlsanity.com/paper/2305.14489/) (2023)
- [Multilingual Coreference Resolution with Harmonized Annotations](https://mlsanity.com/paper/2107.12088/) (2021)
- [Findings of the Shared Task on Multilingual Coreference Resolution](https://mlsanity.com/paper/2209.07841/) (2022)
- [Prague Dependency Treebank -- Consolidated 1.0](https://mlsanity.com/paper/2006.03679/) (2020)
- [CAW-coref: Conjunction-Aware Word-level Coreference Resolution](https://mlsanity.com/paper/2310.06165/) (2023)
- [Findings of the Third Shared Task on Multilingual Coreference Resolution](https://mlsanity.com/paper/2410.15949/) (2024)
- [[Lions: 1] and [Tigers: 2] and [Bears: 3], Oh My! Literary Coreference Annotation with LLMs](https://mlsanity.com/paper/2401.17922/) (2024)
- [CorPipe at CRAC 2024: Predicting Zero Mentions from Raw Text](https://mlsanity.com/paper/2410.02756/) (2024)
- [WinoWhat: A Parallel Corpus of Paraphrased WinoGrande Sentences with Common Sense Categorization](https://mlsanity.com/paper/2503.23779/) (2025)

## Built on by (most cited first, 6 of 6)

- [Findings of the Fifth Shared Task on Multilingual Coreference Resolution: Expanding Datasets for Long-Range Entities](https://mlsanity.com/paper/2605.21369/) (2026)
- [Closing the Gap at CRAC 2026: Two-Stage Adaptation for LLM-Based Multilingual Coreference Resolution](https://mlsanity.com/paper/2605.16984/) (2026)
- [CorPipe at CRAC 2026: Empty Nodes and Cross-Lingual Transfer in Multilingual Coreference Resolution](https://mlsanity.com/paper/2605.30133/) (2026)
- [Multilingual Coreference Resolution via Cycle-Consistent Machine Translation](https://mlsanity.com/paper/2606.05444/) (2026)
- [Human-Like Anaphor Resolution in Large Language Models](https://mlsanity.com/paper/2608.05630/) (2026)
- [Worth Their Weights? Evaluating Generative Models for Discourse Parsing](https://mlsanity.com/paper/2610.17658/) (2026)

## Abstract (arXiv)

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.