mlsanityData to 7 Oct 2026

Cartography Active Learning

arXiv 2109.04282 ↗First posted 9 Sep 2021cs.CL cs.LGcited by 26

Not on the map. Why?

Abstract (arXiv)

We propose Cartography Active Learning (CAL), a novel Active Learning (AL) algorithm that exploits the behavior of the model on individual instances during training as a proxy to find the most informative instances for labeling. CAL is inspired by data maps, which were recently proposed to derive insights into dataset quality (Swayamdipta et al., 2020). We compare our method on popular text classification tasks to commonly used AL strategies, which instead rely on post-training behavior. We demonstrate that CAL is competitive to other common AL methods, showing that training dynamics derived from small seed data can be successfully used for AL. We provide insights into our new AL method by analyzing batch-level statistics utilizing the data maps. Our results further show that CAL results in a more data-efficient learning strategy, achieving comparable or better results with considerably less training data.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 9 Sep 2021.

Builds on

14 of 32 references.

References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  3. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
  4. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  5. 2020Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamicscited by 423
  6. 2021Active Learning by Acquiring Contrastive Examplescited by 154
  7. 2017Deep Active Learning for Named Entity Recognitioncited by 108
  8. 2019Discriminative Active Learningcited by 97
  9. 2018Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Studycited by 90
  10. 2019Diverse mini-batch Active Learningcited by 88
  11. 2017Deep Active Learning over the Long Tailcited by 82
  12. 2018Practical Obstacles to Deploying Active Learningcited by 68
2 more
  1. 2021Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answeringcited by 62
  2. 2018Why Comparing Single Performance Scores Does Not Allow to Draw Conclusions About Machine Learning Approachescited by 15

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 8; 2023: 7; 2024: 6; 2025: 1; 2026: 1.

26 papers.

Sort:

By area:

  1. 2022The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluationcited by 133
  2. 2023Anchor Points: Benchmarking Models with Much Fewer Examplescited by 84
  3. 2022A Survey of Active Learning for Natural Language Processingcited by 40
  4. 2023Exploring Active 3D Object Detection from a Generalization Perspectivecited by 26
  5. 2022ALLSH: Active Learning Guided by Local Sensitivity and Hardnesscited by 23
  6. 2023ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variationcited by 17
  7. 2022Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approachcited by 16
  8. 2022TiDAL: Learning Training Dynamics for Active Learningcited by 11
  9. 2024Empowering Language Models with Active Inquiry for Deeper Understandingcited by 11
  10. 2022Experimental Standards for Deep Learning in Natural Language Processing Researchcited by 9
  11. 2023On the Limitations of Simulating Active Learningcited by 9
  12. 2021LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Frameworkcited by 8
14 more
  1. 2024Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classificationcited by 7
  2. 2023Active Learning for Abstractive Text Summarizationcited by 6
  3. 2023Investigating Multi-source Active Learning for Natural Language Inferencecited by 6
  4. 2021Active learning for reducing labeling effort in text classification taskscited by 4
  5. 2024Automatic Combination of Sample Selection Strategies for Few-Shot Learningcited by 4
  6. 2021Multi-View Active Learning for Short Text Classification in User-Generated Datacited by 3
  7. 2024SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipecited by 3
  8. 2022An Efficient Active Learning Pipeline for Legal Text Classificationcited by 2
  9. 2023Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challengecited by 1
  10. 2022Radically Lower Data-Labeling Costs for Visually Rich Document Extraction Modelscited by 0
  11. 2024Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained Language Modelscited by 0
  12. 2024ALVIN: Active Learning Via INterpolationcited by 0
  13. 2025Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Gridcited by 0
  14. 2026An Assessment of Human vs. Model Uncertainty in Soft-Label Learning and Calibrationcited by 0