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A Survey of Active Learning for Natural Language Processing

arXiv 2210.10109 ↗First posted 18 Oct 2022cs.CLcited by 40

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Abstract (arXiv)

In this work, we provide a survey of active learning (AL) for its applications in natural language processing (NLP). In addition to a fine-grained categorization of query strategies, we also investigate several other important aspects of applying AL to NLP problems. These include AL for structured prediction tasks, annotation cost, model learning (especially with deep neural models), and starting and stopping AL. Finally, we conclude with a discussion of related topics and future directions.

Key facts. Cited by 40 papers in the map (data through 14 Oct 2026). Builds on 85 of its 296 references that are in the map. First posted on arXiv 18 Oct 2022.

Builds on

85 of 296 references.

References · 85

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  2. 2020Don't Stop Pretraining: Adapt Language Models to Domains and Taskscited by 1,565
  3. 2017Deep Bayesian Active Learning with Image Datacited by 819
  4. 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
  5. 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 610
  6. 2020A Survey of Deep Active Learningcited by 554
  7. 2020Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamicscited by 425
  8. 2019Learning Loss for Active Learningcited by 336
  9. 2019Variational Adversarial Active Learningcited by 284
  10. 2019Competence-based Curriculum Learning for Neural Machine Translationcited by 202
  11. 2021Active Learning by Acquiring Contrastive Examplescited by 156
  12. 2021Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Bettercited by 154
38 more
  1. 2020Cold-start Active Learning through Self-supervised Language Modelingcited by 128
  2. 2021Batch Active Learning at Scalecited by 127
  3. 2017Deep Active Learning for Named Entity Recognitioncited by 108
  4. 2019Discriminative Active Learningcited by 98
  5. 2018Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Studycited by 90
  6. 2019Diverse mini-batch Active Learningcited by 89
  7. 2022A Comparative Survey of Deep Active Learningcited by 89
  8. 2017Deep Active Learning over the Long Tailcited by 84
  9. 2017Learning how to Active Learn: A Deep Reinforcement Learning Approachcited by 79
  10. 2018Practical Obstacles to Deploying Active Learningcited by 68
  11. 2021Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answeringcited by 62
  12. 2021Revisiting Uncertainty-based Query Strategies for Active Learning with Transformerscited by 60
  13. 2020SeqMix: Augmenting Active Sequence Labeling via Sequence Mixupcited by 52
  14. 2020A Survey of Active Learning for Text Classification using Deep Neural Networkscited by 50
  15. 2020Predicting Performance for Natural Language Processing Taskscited by 49
  16. 2021Putting Humans in the Natural Language Processing Loop: A Surveycited by 40
  17. 2021Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimatescited by 39
  18. 2019Low-resource Deep Entity Resolution with Transfer and Active Learningcited by 37
  19. 2019Sampling Bias in Deep Active Classification: An Empirical Studycited by 36
  20. 2020ALICE: Active Learning with Contrastive Natural Language Explanationscited by 36
  21. 2022Active Learning Helps Pretrained Models Learn the Intended Taskcited by 33
  22. 2021On the Importance of Effectively Adapting Pretrained Language Models for Active Learningcited by 32
  23. 2017Learning Algorithms for Active Learningcited by 30
  24. 2018Active Learning with Partial Feedbackcited by 27
  25. 2021Cartography Active Learningcited by 26
  26. 2016Active Learning for Speech Recognition: the Power of Gradientscited by 25
  27. 2020Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learningcited by 25
  28. 2020Active Learning: Problem Settings and Recent Developmentscited by 23
  29. 2022ALLSH: Active Learning Guided by Local Sensitivity and Hardnesscited by 23
  30. 2021Diversity Enhanced Active Learning with Strictly Proper Scoring Rulescited by 22
  31. 2014A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stoppingcited by 21
  32. 2018Active Learning for Interactive Neural Machine Translation of Data Streamscited by 21
  33. 2020Reinforced Curriculum Learning on Pre-trained Neural Machine Translation Modelscited by 20
  34. 2020Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Spacecited by 17
  35. 2022Multi-task Active Learning for Pre-trained Transformer-based Modelscited by 16
  36. 2020Active Imitation Learning with Noisy Guidancecited by 15
  37. 2022PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learningcited by 14
  38. 2021Phrase-level Active Learning for Neural Machine Translationcited by 13

50 most cited of 85 All 85 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 15; 2024: 13; 2025: 7; 2026: 4.

40 papers.

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  1. 2023Active Learning Principles for In-Context Learning with Large Language Modelscited by 54
  2. 2024A Survey on Deep Active Learning: Recent Advances and New Frontierscited by 54
  3. 2024Feedback Efficient Online Fine-Tuning of Diffusion Modelscited by 53
  4. 2023Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selectioncited by 37
  5. 2024VideoCoT: A Video Chain-of-Thought Dataset with Active Annotation Toolcited by 35
  6. 2023Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Costcited by 33
  7. 2024ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarioscited by 18
  8. 2024On the Limitations of Large Language Models (LLMs): False Attributioncited by 13
  9. 2023Learning Hidden Markov Models Using Conditional Samplescited by 12
  10. 2023D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Biascited by 12
  11. 2023On the Limitations of Simulating Active Learningcited by 9
  12. 2024D2PO: Discriminator-Guided DPO with Response Evaluation Modelscited by 9
28 more
  1. 2024Value Preferences Estimation and Disambiguation in Hybrid Participatory Systemscited by 7
  2. 2023Investigating Multi-source Active Learning for Natural Language Inferencecited by 6
  3. 2023An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasetscited by 4
  4. 2023Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervisioncited by 4
  5. 2026DriftGuard: Safety-Aware Multi-Monitor Detection and Selective Adaptation for Evolving Toxicity Moderationcited by 4
  6. 2023Active Continual Learning: On Balancing Knowledge Retention and Learnabilitycited by 3
  7. 2023Active Learning for Natural Language Generationcited by 3
  8. 2023Transferring Procedural Knowledge across Commonsense Taskscited by 2
  9. 2024Active Learning for NLP with Large Language Modelscited by 2
  10. 2024Scoping Review of Active Learning Strategies and their Evaluation Environments for Entity Recognition Taskscited by 2
  11. 2024Interactive Machine Teaching by Labeling Rules and Instancescited by 2
  12. 2025DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learningcited by 2
  13. 2025PromptAL: Sample-Aware Dynamic Soft Prompts for Few-Shot Active Learningcited by 2
  14. 2022MAViC: Multimodal Active Learning for Video Captioningcited by 1
  15. 2023XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learnerscited by 1
  16. 2024CALICO: Confident Active Learning with Integrated Calibrationcited by 1
  17. 2025A Survey of the State-of-the-Art in Conversational Question Answering Systemscited by 1
  18. 2026Active Learning for Decision Trees with Provable Guaranteescited by 1
  19. 2023Mining of Single-Class by Active Learning for Semantic Segmentationcited by 0
  20. 2023Making Pre-trained Language Models both Task-solvers and Self-calibratorscited by 0
  21. 2024Active Learning with Task Adaptation Pre-training for Speech Emotion Recognitioncited by 0
  22. 2024Salutary Labeling with Zero Human Annotationcited by 0
  23. 2025VNJPTranslate: A comprehensive pipeline for Vietnamese-Japanese translationcited by 0
  24. 2025Active Learning Methods for Efficient Data Utilization and Model Performance Enhancementcited by 0
  25. 2025Actively evaluating and learning the distinctions that matter: Vaccine safety signal detection from emergency triage notescited by 0
  26. 2025ART: Adaptive Relation Tuning for Generalized Relation Predictioncited by 0
  27. 2026Task-Adaptive Embedding Refinement via Test-time LLM Guidancecited by 0
  28. 2026Improving Selective Classification with Pairwise Queries for Binary Classificationcited by 0