# GLiREL -- Generalist Model for Zero-Shot Relation Extraction

arXiv: https://arxiv.org/abs/2501.03172 · HTML: https://mlsanity.com/paper/2501.03172/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 52 references that are in the map.
- First posted on arXiv 6 Jan 2025.

## Builds on (most cited first, 24 of 24)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale](https://mlsanity.com/paper/2406.17557/) (2024)
- [Zero-Shot Relation Extraction via Reading Comprehension](https://mlsanity.com/paper/1706.04115/) (2017)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://mlsanity.com/paper/1909.07755/) (2019)
- [Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!](https://mlsanity.com/paper/2303.08559/) (2023)
- [GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction](https://mlsanity.com/paper/2310.03668/) (2023)
- [FewRel 2.0: Towards More Challenging Few-Shot Relation Classification](https://mlsanity.com/paper/1910.07124/) (2019)
- [Coreferential Reasoning Learning for Language Representation](https://mlsanity.com/paper/2004.06870/) (2020)
- [Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction](https://mlsanity.com/paper/2109.03659/) (2021)
- [Reasoning about Entailment with Neural Attention](https://mlsanity.com/paper/1509.06664/) (2015)
- [Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction](https://mlsanity.com/paper/2205.12696/) (2022)
- [ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning](https://mlsanity.com/paper/2104.04697/) (2021)
- [NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data](https://mlsanity.com/paper/2402.15343/) (2024)
- [How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?](https://mlsanity.com/paper/2305.01555/) (2023)
- [Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes](https://mlsanity.com/paper/2104.08481/) (2021)
- [Lifted Rule Injection for Relation Embeddings](https://mlsanity.com/paper/1606.08359/) (2016)
- [Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet Extraction](https://mlsanity.com/paper/2401.13598/) (2024)
- [RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction](https://mlsanity.com/paper/2306.04954/) (2023)
- [Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors](https://mlsanity.com/paper/2404.17807/) (2024)
- [Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation](https://mlsanity.com/paper/2112.04539/) (2021)
- [GLiNER multi-task: Generalist Lightweight Model for Various Information Extraction Tasks](https://mlsanity.com/paper/2406.12925/) (2024)
- [Comparing Convolutional Neural Networks to Traditional Models for Slot Filling](https://mlsanity.com/paper/1603.05157/) (2016)

## Built on by (most cited first, 12 of 12)

- [GLiClass: Generalist Lightweight Model for Sequence Classification Tasks](https://mlsanity.com/paper/2508.07662/) (2025)
- [GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface](https://mlsanity.com/paper/2507.18546/) (2025)
- [GLiDRE: Generalist Lightweight model for Document-level Relation Extraction](https://mlsanity.com/paper/2508.00757/) (2025)
- [CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts](https://mlsanity.com/paper/2602.17663/) (2026)
- [Overview of HIPE-2026: Person-Place Relation Extraction from Multilingual Historical Texts](https://mlsanity.com/paper/2606.25935/) (2026)
- [LLM-based Atomic Propositions help weak extractors: Evaluation of a Propositioner for triplet extraction](https://mlsanity.com/paper/2604.02866/) (2026)
- [GLiNER-Relex: A Unified Framework for Joint Named Entity Recognition and Relation Extraction](https://mlsanity.com/paper/2605.10108/) (2026)
- [A Tale of LLMs and Induced Small Proxies: Scalable Small Language Models for Knowledge Mining](https://mlsanity.com/paper/2510.01427/) (2025)
- [A Study on Building Efficient Zero-Shot Relation Extraction Models](https://mlsanity.com/paper/2603.01266/) (2026)
- [SAGE: A Unified Algebra and Self-Adaptive Execution for AI Functions in SQL](https://mlsanity.com/paper/2608.20630/) (2026)
- [HANIA: Planner-Guided Multimodal Graph Evidence Selection for Grounded Question Answering](https://mlsanity.com/paper/2608.29088/) (2026)
- [Calibrated Typed Decisions for Entity, Relation, and Event Extraction](https://mlsanity.com/paper/2610.17113/) (2026)

## Abstract (arXiv)

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.