# GLiDRE: Generalist Lightweight model for Document-level Relation Extraction

arXiv: https://arxiv.org/abs/2508.00757 · HTML: https://mlsanity.com/paper/2508.00757/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 19 of 19)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale](https://mlsanity.com/paper/2406.17557/) (2024)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://mlsanity.com/paper/1906.06127/) (2019)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction](https://mlsanity.com/paper/2310.03668/) (2023)
- [Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling](https://mlsanity.com/paper/2010.11304/) (2020)
- [FewRel 2.0: Towards More Challenging Few-Shot Relation Classification](https://mlsanity.com/paper/1910.07124/) (2019)
- [RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction](https://mlsanity.com/paper/2203.09101/) (2022)
- [Zero-shot Temporal Relation Extraction with ChatGPT](https://mlsanity.com/paper/2304.05454/) (2023)
- [Document-Level N-ary Relation Extraction with Multiscale Representation Learning](https://mlsanity.com/paper/1904.02347/) (2019)
- [ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning](https://mlsanity.com/paper/2104.04697/) (2021)
- [Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes](https://mlsanity.com/paper/2104.08481/) (2021)
- [Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors](https://mlsanity.com/paper/2404.17807/) (2024)
- [GLiREL -- Generalist Model for Zero-Shot Relation Extraction](https://mlsanity.com/paper/2501.03172/) (2025)
- [Few-Shot Document-Level Relation Extraction](https://mlsanity.com/paper/2205.02048/) (2022)
- [RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction](https://mlsanity.com/paper/2310.15743/) (2023)

## Built on by (most cited first, 3 of 3)

- [GLiClass: Generalist Lightweight Model for Sequence Classification Tasks](https://mlsanity.com/paper/2508.07662/) (2025)
- [GLiNER-Relex: A Unified Framework for Joint Named Entity Recognition and Relation Extraction](https://mlsanity.com/paper/2605.10108/) (2026)
- [Sub-Billion, Super-Frontier: Small Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction](https://mlsanity.com/paper/2606.22606/) (2026)

## Abstract (arXiv)

Relation Extraction (RE) is a fundamental task in Natural Language Processing, and its document-level variant poses significant challenges, due to complex interactions between entities across sentences. While supervised models have achieved strong results in fully resourced settings, their behavior with limited training data remains insufficiently studied. We introduce GLiDRE, a new compact model for document-level relation extraction, designed to work efficiently in both supervised and few-shot settings. Experiments in both low-resource supervised training and few-shot meta-learning benchmarks show that our approach outperforms existing methods in data-constrained scenarios, establishing a new state-of-the-art in few-shot document-level relation extraction. Our code will be publicly available.