We introduce GLiREL (Generalist Lightweight model for zero-shot Relation Extraction), an efficient architecture and training paradigm for zero-shot relation classification. Inspired by recent advancements in zero-shot named entity recognition, this work presents 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.
Cite
@article{arxiv.2501.03172,
title = {GLiREL -- Generalist Model for Zero-Shot Relation Extraction},
author = {Jack Boylan and Chris Hokamp and Demian Gholipour Ghalandari},
journal= {arXiv preprint arXiv:2501.03172},
year = {2025}
}