@inproceedings{boylan-etal-2025-glirel,
    title = "{GL}i{REL} - Generalist Model for Zero-Shot Relation Extraction",
    author = "Boylan, Jack  and
      Hokamp, Chris  and
      Ghalandari, Demian Gholipour",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.418/",
    pages = "8230--8245",
    ISBN = "979-8-89176-189-6",
    abstract = "We introduce GLiREL, an efficient architecture and training paradigm for zero-shot relation classification. Identifying relationships between entities is a key task in information extraction pipelines. The zero-shot setting for relation extraction, where a taxonomy of relations is not pre-specified, has proven to be particularly challenging because of the computational complexity of inference, and because of the lack of labeled training data with sufficient coverage. Existing approaches rely upon distant supervision using auxiliary models to generate training data for unseen labels, upon very large general-purpose large language models (LLMs), or upon complex pipelines models with multiple inference stages. Inspired by the recent advancements in zero-shot named entity recognition, this paper introduces an approach to efficiently and accurately predict zero-shot relationship labels between multiple entities in a single forward pass. Experiments using the FewRel and WikiZSL benchmarks demonstrate that our approach achieves state-of-the-art results on the zero-shot relation classification task. In addition, we contribute a protocol for synthetically-generating datasets with diverse relation labels."
}