mlsanityData to 7 Oct 2026

Few-NERD: A Few-Shot Named Entity Recognition Dataset

arXiv 2105.07464 ↗First posted 16 May 2021cs.CL cs.AI cs.LGcited by 150

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Abstract (arXiv)

Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark data specifically focused on the practical and challenging task. Current approaches collect existing supervised NER datasets and re-organize them to the few-shot setting for empirical study. These strategies conventionally aim to recognize coarse-grained entity types with few examples, while in practice, most unseen entity types are fine-grained. In this paper, we present Few-NERD, a large-scale human-annotated few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types. Few-NERD consists of 188,238 sentences from Wikipedia, 4,601,160 words are included and each is annotated as context or a part of a two-level entity type. To the best of our knowledge, this is the first few-shot NER dataset and the largest human-crafted NER dataset. We construct benchmark tasks with different emphases to comprehensively assess the generalization capability of models. Extensive empirical results and analysis show that Few-NERD is challenging and the problem requires further research. We make Few-NERD public at https://ningding97.github.io/fewnerd/.

Key facts. Cited by 150 papers in the map (data through 7 Oct 2026). Builds on 20 of its 42 references that are in the map. First posted on arXiv 16 May 2021.

Builds on

20 of 42 references.

References · 20

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
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  4. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  5. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  6. 2016Neural Architectures for Named Entity Recognitioncited by 548
  7. 2016End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRFcited by 341
  8. 2018FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluationcited by 254
  9. 2015Named Entity Recognition with Bidirectional LSTM-CNNscited by 201
  10. 2019A Unified MRC Framework for Named Entity Recognitioncited by 199
  11. 2020MAVEN: A Massive General Domain Event Detection Datasetcited by 103
  12. 2018Ultra-Fine Entity Typingcited by 87
8 more
  1. 2020Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Networkcited by 83
  2. 2018Few-shot classification in Named Entity Recognition Taskcited by 64
  3. 2019KBQA: Learning Question Answering over QA Corpora and Knowledge Basescited by 44
  4. 2020Few-Shot Named Entity Recognition: A Comprehensive Studycited by 41
  5. 2021Prototypical Representation Learning for Relation Extractioncited by 33
  6. 2014Context-Dependent Fine-Grained Entity Type Taggingcited by 30
  7. 2019NNE: A Dataset for Nested Named Entity Recognition in English Newswirecited by 25
  8. 2018Few-shot Learning for Named Entity Recognition in Medical Textcited by 19

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Citing papers per year: 2021: 10; 2022: 34; 2023: 45; 2024: 28; 2025: 17; 2026: 16.

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  1. 2024LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitaskscited by 367
  2. 2024Long-context LLMs Struggle with Long In-context Learningcited by 285
  3. 2021PTR: Prompt Tuning with Rules for Text Classificationcited by 238
  4. 2023Large Language Models for Generative Information Extraction: A Surveycited by 183
  5. 2021OpenPrompt: An Open-source Framework for Prompt-learningcited by 148
  6. 2023Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!cited by 119
  7. 2022State-of-the-art generalisation research in NLP: A taxonomy and reviewcited by 97
  8. 2023Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluationscited by 96
  9. 2023MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversationcited by 94
  10. 2023KoLA: Carefully Benchmarking World Knowledge of Large Language Modelscited by 85
  11. 2026Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Modelscited by 78
  12. 2021Template-free Prompt Tuning for Few-shot NERcited by 73
38 more
  1. 2023UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognitioncited by 70
  2. 2023ClusterLLM: Large Language Models as a Guide for Text Clusteringcited by 69
  3. 2021CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learningcited by 64
  4. 2021Prompt-Learning for Fine-Grained Entity Typingcited by 62
  5. 2024Towards Lifelong Learning of Large Language Models: A Surveycited by 60
  6. 2022Prototypical Verbalizer for Prompt-based Few-shot Tuningcited by 53
  7. 2021Packed Levitated Marker for Entity and Relation Extractioncited by 49
  8. 2021Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NERcited by 38
  9. 2023When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Taskscited by 37
  10. 2022Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learningcited by 36
  11. 2023PromptNER: Prompting For Named Entity Recognitioncited by 35
  12. 2024Conformal Prediction Sets Improve Human Decision Makingcited by 35
  13. 2024NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Datacited by 34
  14. 2022WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Typescited by 33
  15. 2024A Survey on Data Augmentation in Large Model Eracited by 31
  16. 2022Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbationcited by 28
  17. 2021LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Promptingcited by 27
  18. 2021An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labelingcited by 26
  19. 2024ADELIE: Aligning Large Language Models on Information Extractioncited by 24
  20. 2022Decomposed Meta-Learning for Few-Shot Named Entity Recognitioncited by 22
  21. 2022Label Semantics for Few Shot Named Entity Recognitioncited by 21
  22. 2022Parallel Instance Query Network for Named Entity Recognitioncited by 19
  23. 2023SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understandingcited by 18
  24. 2022Exploring the Role of Task Transferability in Large-Scale Multi-Task Learningcited by 17
  25. 2022Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypescited by 17
  26. 2023FiNER-ORD: Financial Named Entity Recognition Open Research Datasetcited by 17
  27. 2023PromptNER: Prompt Locating and Typing for Named Entity Recognitioncited by 17
  28. 2023ConPET: Continual Parameter-Efficient Tuning for Large Language Modelscited by 16
  29. 2023FindVehicle and VehicleFinder: A NER dataset for natural language-based vehicle retrieval and a keyword-based cross-modal vehicle retrieval systemcited by 15
  30. 2024MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Taskscited by 14
  31. 2023MESED: A Multi-modal Entity Set Expansion Dataset with Fine-grained Semantic Classes and Hard Negative Entitiescited by 13
  32. 2023Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Modelscited by 13
  33. 2022HiNER: A Large Hindi Named Entity Recognition Datasetcited by 12
  34. 2022Prompt-Based Metric Learning for Few-Shot NERcited by 12
  35. 2023Hierarchical Verbalizer for Few-Shot Hierarchical Text Classificationcited by 11
  36. 2024LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognitioncited by 11
  37. 2021Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognitioncited by 10
  38. 2022Information Extraction in Low-Resource Scenarios: Survey and Perspectivecited by 10

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