mlsanityData to 7 Oct 2026

Prompt-Based Metric Learning for Few-Shot NER

arXiv 2211.04337 ↗First posted 8 Nov 2022cs.CLcited by 12

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Abstract (arXiv)

Few-shot named entity recognition (NER) targets generalizing to unseen labels and/or domains with few labeled examples. Existing metric learning methods compute token-level similarities between query and support sets, but are not able to fully incorporate label semantics into modeling. To address this issue, we propose a simple method to largely improve metric learning for NER: 1) multiple prompt schemas are designed to enhance label semantics; 2) we propose a novel architecture to effectively combine multiple prompt-based representations. Empirically, our method achieves new state-of-the-art (SOTA) results under 16 of the 18 considered settings, substantially outperforming the previous SOTA by an average of 8.84% and a maximum of 34.51% in relative gains of micro F1. Our code is available at https://github.com/AChen-qaq/ProML.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 22 of its 37 references that are in the map. First posted on arXiv 8 Nov 2022.

Builds on

22 of 37 references.

References · 22

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  3. 2016Matching Networks for One Shot Learningcited by 2,246
  4. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  5. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 668
  6. 2018FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluationcited by 254
  7. 2021Structured Prediction as Translation between Augmented Natural Languagescited by 183
  8. 2021Template-Based Named Entity Recognition Using BARTcited by 153
  9. 2021Few-NERD: A Few-Shot Named Entity Recognition Datasetcited by 150
  10. 2018Meta-Learning for Low-Resource Neural Machine Translationcited by 105
  11. 2019TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learningcited by 90
  12. 2020Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Networkcited by 83
10 more
  1. 2021Template-free Prompt Tuning for Few-shot NERcited by 73
  2. 2018Few-shot classification in Named Entity Recognition Taskcited by 64
  3. 2019Induction Networks for Few-Shot Text Classificationcited by 64
  4. 2021CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learningcited by 64
  5. 2019Few-shot Text Classification with Distributional Signaturescited by 58
  6. 2020Few-Shot Named Entity Recognition: A Comprehensive Studycited by 41
  7. 2021Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NERcited by 38
  8. 2021An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labelingcited by 26
  9. 2022Decomposed Meta-Learning for Few-Shot Named Entity Recognitioncited by 22
  10. 2022Label Semantics for Few Shot Named Entity Recognitioncited by 21

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 4; 2026: 1.

12 papers.

  1. 2023PromptNER: Prompting For Named Entity Recognitioncited by 35
  2. 2024Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learningcited by 6
  3. 2024Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognitioncited by 2
  4. 2024CLLMFS: A Contrastive Learning enhanced Large Language Model Framework for Few-Shot Named Entity Recognitioncited by 2
  5. 2024Familiarity: Better Evaluation of Zero-Shot Named Entity Recognition by Quantifying Label Shifts in Synthetic Training Datacited by 2
  6. 2025FiNERweb: Datasets and Artifacts for Scalable Multilingual Named Entity Recognitioncited by 2
  7. 2024Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstructioncited by 1
  8. 2025FewTopNER: Integrating Few-Shot Learning with Topic Modeling and Named Entity Recognition in a Multilingual Frameworkcited by 1
  9. 2026Do LLMs Judge Distantly Supervised Named Entity Labels Well? Constructing the JudgeWEL Datasetcited by 1
  10. 2024Few-Shot Domain Adaptation for Named-Entity Recognition via Joint Constrained k-Means and Subspace Selectioncited by 0
  11. 2025Improving Low-Resource Sequence Labeling with Knowledge Fusion and Contextual Label Explanationscited by 0
  12. 2025Named Entity Recognition in COVID-19 tweets with Entity Knowledge Augmentationcited by 0