mlsanityData to 14 Oct 2026

GLiDRE: Generalist Lightweight model for Document-level Relation Extraction

arXiv 2508.00757 ↗First posted 1 Aug 2025cs.CLcited by 3

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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.

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

19 of 38 references.

References · 19

  1. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  2. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
  3. 2024The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scalecited by 1,160
  4. 2018Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Constructioncited by 322
  5. 2018FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluationcited by 254
  6. 2019DocRED: A Large-Scale Document-Level Relation Extraction Datasetcited by 239
  7. 2023InstructUIE: Multi-task Instruction Tuning for Unified Information Extractioncited by 170
  8. 2023GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extractioncited by 119
  9. 2020Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Poolingcited by 115
  10. 2019FewRel 2.0: Towards More Challenging Few-Shot Relation Classificationcited by 94
  11. 2022RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extractioncited by 60
  12. 2023Zero-shot Temporal Relation Extraction with ChatGPTcited by 53
7 more
  1. 2019Document-Level N-ary Relation Extraction with Multiscale Representation Learningcited by 52
  2. 2021ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learningcited by 46
  3. 2021Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemescited by 27
  4. 2024Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractorscited by 13
  5. 2025GLiREL -- Generalist Model for Zero-Shot Relation Extractioncited by 12
  6. 2022Few-Shot Document-Level Relation Extractioncited by 11
  7. 2023RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extractioncited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025GLiClass: Generalist Lightweight Model for Sequence Classification Taskscited by 15
  2. 2026GLiNER-Relex: A Unified Framework for Joint Named Entity Recognition and Relation Extractioncited by 1
  3. 2026Sub-Billion, Super-Frontier: Small Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extractioncited by 0