---
title: "CorefInst: Leveraging LLMs for Multilingual — Arslan 2026"
description: "The paper addresses multilingual coreference resolution (CR)."
authors: ["Tuğba Pamay Arslan", "Emircan Erol", "Gülşen Eryiğit"]
year: 2026
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/2026.tacl-1.4/"
doi: "10.1162/tacl.a.593"
arxiv: "2509.17505"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/arslan-2026-corefinst-leveraging-llms-multilingual-coreference/"
---
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# CorefInst: Leveraging LLMs for Multilingual Coreference Resolution

**Authors:** Tuğba Pamay Arslan, Emircan Erol, [Gülşen Eryiğit](https://md.research.elamur.ai/free/en/authors/gulsen-eryi-it/) · **Year:** 2026 · **Venue:** Transactions of the Association for Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.tacl-1.4/) · [PDF](https://aclanthology.org/2026.tacl-1.4.pdf) · [DOI 10.1162/tacl.a.593](https://doi.org/10.1162/tacl.a.593) · [arXiv 2509.17505](https://arxiv.org/abs/2509.17505) · [OpenAlex](https://openalex.org/W7118648461)

**Citations:** 1 citations (openalex-snapshot · 2026-10-14)

## Summary
The paper addresses multilingual coreference resolution (CR). This task consists of finding mentions of the same entity in a text and grouping them into a cluster. The authors also take zero mentions into account, that is, omitted pronouns in pro-drop languages. Typical CR systems are built on encoder models with a specialized architecture. Instead, the authors are the first to instruction-tune decoder-only LLMs. The model receives an instruction and a text with marked mentions, and in response generates the same sentences with the coreference links annotated. To make the model follow the specified format, controlled inference is proposed, and a separate step assembles the resulting links into clusters. First, three models and five instruction variants were compared. The best pair, named CorefInst and based on Llama 3.1, was evaluated on CorefUD v1.2. After full fine-tuning, it outperformed the strongest multilingual system, CorPipe24, in its single-stage variant by about 2 pp on average. The model is particularly good at recognizing links of zero mentions. The work shows that general-purpose LLMs can replace narrowly specialized CR architectures. This also holds for languages where the subject is frequently omitted.

## Key points
- Llama 3.1, Gemma 2 and Mistral 0.3 were compared with five instruction sets. Several sets, besides the term "coreferential", contain the word "coherent" and an example of a pair of mentions that are not coreferent.
- The evaluation was carried out on CorefUD v1.2 (21 datasets, 15 languages). The average advantage over CorPipe24-single is 1.8 pp and is statistically significant. For individual languages, the gain ranges from 0.6 to 11.5 pp.
- The fully fine-tuned model clearly outperforms the 5-shot variant. Hence, off-the-shelf LLMs cannot yet handle this task without fine-tuning.
- The models were fine-tuned via LoRA on quantized versions. The system covers only the mention-linking stage, while automatic mention detection is left by the authors for future work.
- The main limitation is that the document is processed in partially overlapping fragments. Because of this, a coreference chain may break, and a single entity may split into several clusters.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{arslan2026corefinst,
  title = {{CorefInst: Leveraging LLMs for Multilingual Coreference Resolution}},
  author = {Arslan, Tuğba Pamay and Erol, Emircan and Eryiğit, Gülşen},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2026},
  doi = {10.1162/tacl.a.593},
  eprint = {2509.17505},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1162/tacl.a.593}
}
```

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