GLiREL -- Generalist Model for Zero-Shot Relation Extraction
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913637225988096 |
|---|---|
| author | Boylan, Jack Hokamp, Chris Ghalandari, Demian Gholipour |
| author_facet | Boylan, Jack Hokamp, Chris Ghalandari, Demian Gholipour |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03172 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GLiREL -- Generalist Model for Zero-Shot Relation Extraction Boylan, Jack Hokamp, Chris Ghalandari, Demian Gholipour Computation and Language Artificial Intelligence Machine Learning 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. |
| title | GLiREL -- Generalist Model for Zero-Shot Relation Extraction |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2501.03172 |