@inproceedings{gan-etal-2025-low,
    title = "Low-Hallucination and Efficient Coreference Resolution with {LLM}s",
    author = "Gan, Yujian  and
      Liang, Yuan  and
      Xie, Jinxia  and
      Lin, Yanni  and
      Yu, Juntao  and
      Poesio, Massimo",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-luhme/2025.findings-emnlp.934/",
    doi = "10.18653/v1/2025.findings-emnlp.934",
    pages = "17243--17256",
    ISBN = "979-8-89176-335-7",
    abstract = "Large Language Models (LLMs) have shown promising results in coreference resolution, especially after fine-tuning. However, recent generative approaches face a critical issue: hallucinations{---}where the model generates content not present in the original input. These hallucinations make evaluation difficult and decrease overall performance. To address this issue, we analyze the underlying causes of hallucinations and propose a low-hallucination and efficient solution. Specifically, we introduce Efficient Constrained Decoding for Coreference Resolution, which maintains strong robustness while significantly improving computational efficiency. On the English OntoNotes development set, our approach achieved slightly better performance than previous state-of-the-art methods, while requiring substantially fewer parameters."
}