mlsanityData to 14 Oct 2026

Information Extraction in Low-Resource Scenarios: Survey and Perspective

arXiv 2202.08063 ↗First posted 16 Feb 2022cs.CL cs.AI cs.IRcited by 10

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Abstract (arXiv)

Information Extraction (IE) seeks to derive structured information from unstructured texts, often facing challenges in low-resource scenarios due to data scarcity and unseen classes. This paper presents a review of neural approaches to low-resource IE from traditional and LLM-based perspectives, systematically categorizing them into a fine-grained taxonomy. Then we conduct empirical study on LLM-based methods compared with previous state-of-the-art models, and discover that (1) well-tuned LMs are still predominant; (2) tuning open-resource LLMs and ICL with GPT family is promising in general; (3) the optimal LLM-based technical solution for low-resource IE can be task-dependent. In addition, we discuss low-resource IE with LLMs, highlight promising applications, and outline potential research directions. This survey aims to foster understanding of this field, inspire new ideas, and encourage widespread applications in both academia and industry.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 82 of its 260 references that are in the map. First posted on arXiv 16 Feb 2022.

Builds on

82 of 260 references.

References · 82

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  5. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  6. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  7. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  8. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  9. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  10. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,031
  11. 2020Meta-Learning in Neural Networks: A Surveycited by 950
  12. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
38 more
  1. 2021Deep Long-Tailed Learning: A Surveycited by 417
  2. 2021Pre-Trained Models: Past, Present and Futurecited by 319
  3. 2018A Survey on Deep Learning for Named Entity Recognitioncited by 282
  4. 2019Matching the Blanks: Distributional Similarity for Relation Learningcited by 272
  5. 2019Entity, Relation, and Event Extraction with Contextualized Span Representationscited by 262
  6. 2018FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluationcited by 254
  7. 2021PTR: Prompt Tuning with Rules for Text Classificationcited by 238
  8. 2015Named Entity Recognition with Bidirectional LSTM-CNNscited by 201
  9. 2019A Unified MRC Framework for Named Entity Recognitioncited by 199
  10. 2021Structured Prediction as Translation between Augmented Natural Languagescited by 183
  11. 2021KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extractioncited by 180
  12. 2020Event Extraction by Answering (Almost) Natural Questionscited by 177
  13. 2020A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarioscited by 171
  14. 2020A Review of Generalized Zero-Shot Learning Methodscited by 159
  15. 2021Document-Level Event Argument Extraction by Conditional Generationcited by 155
  16. 2021Template-Based Named Entity Recognition Using BARTcited by 153
  17. 2021Few-NERD: A Few-Shot Named Entity Recognition Datasetcited by 152
  18. 2021Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extractioncited by 129
  19. 2020MAVEN: A Massive General Domain Event Detection Datasetcited by 104
  20. 2021An Empirical Survey of Data Augmentation for Limited Data Learning in NLPcited by 101
  21. 2019Entity-Relation Extraction as Multi-Turn Question Answeringcited by 96
  22. 2020TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Taskcited by 96
  23. 2019FewRel 2.0: Towards More Challenging Few-Shot Relation Classificationcited by 94
  24. 2020BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervisioncited by 91
  25. 2020Automated Concatenation of Embeddings for Structured Predictioncited by 86
  26. 2021DEGREE: A Data-Efficient Generation-Based Event Extraction Modelcited by 83
  27. 2019Multi-Sentence Argument Linkingcited by 81
  28. 2021Template-free Prompt Tuning for Few-shot NERcited by 73
  29. 2017Zero-Shot Transfer Learning for Event Extractioncited by 66
  30. 2018Learning Named Entity Tagger using Domain-Specific Dictionarycited by 65
  31. 2019Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networkscited by 61
  32. 2020Cross-media Structured Common Space for Multimedia Event Extractioncited by 61
  33. 2019Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detectioncited by 54
  34. 2021GenIE: Generative Information Extractioncited by 53
  35. 2021Packed Levitated Marker for Entity and Relation Extractioncited by 49
  36. 2019CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learningcited by 47
  37. 2021ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learningcited by 46
  38. 2021MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NERcited by 42

50 most cited of 82 All 82 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 4; 2026: 4.

10 papers.

  1. 2025PARSE: LLM Driven Schema Optimization for Reliable Entity Extractioncited by 6
  2. 2025Explaining Large Language Models with gSMILEcited by 4
  3. 2026G-Long: Graph-Enhanced Memory Management for Efficient Long-Term Dialogue Agentscited by 1
  4. 2022STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extractioncited by 0
  5. 2024Granting GPT-4 License and Opportunity: Enhancing Accuracy and Confidence Estimation for Few-Shot Event Detectioncited by 0
  6. 2025Effective and Efficient Schema-aware Information Extraction Using On-Device Large Language Modelscited by 0
  7. 2025Supervised Fine Tuning of Large Language Models for Domain Specific Knowledge Graph Construction:A Case Study on Hunan's Historical Celebritiescited by 0
  8. 2026A Study on Building Efficient Zero-Shot Relation Extraction Modelscited by 0
  9. 2026In Data or Invisible: Toward a Better Digital Representation of Low-Resource Languages with Knowledge Graphscited by 0
  10. 2026Quality Metrics for LLM-Generated Asset Administration Shells: A Perturbation-Based Evaluation Approachcited by 0