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Dynamic Data Selection for Curriculum Learning via Ability Estimation

arXiv 2011.00080 ↗First posted 30 Oct 2020cs.CL cs.LGcited by 21

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Abstract (arXiv)

Curriculum learning methods typically rely on heuristics to estimate the difficulty of training examples or the ability of the model. In this work, we propose replacing difficulty heuristics with learned difficulty parameters. We also propose Dynamic Data selection for Curriculum Learning via Ability Estimation (DDaCLAE), a strategy that probes model ability at each training epoch to select the best training examples at that point. We show that models using learned difficulty and/or ability outperform heuristic-based curriculum learning models on the GLUE classification tasks.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Oct 2020.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 7; 2024: 4; 2025: 1; 2026: 3.

21 papers.

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  1. 2021KLUE: Korean Language Understanding Evaluationcited by 113
  2. 2021Comparing Test Sets with Item Response Theorycited by 55
  3. 2025Fluid Language Model Benchmarkingcited by 30
  4. 2023CLIMB: Curriculum Learning for Infant-inspired Model Buildingcited by 20
  5. 2022py-irt: A Scalable Item Response Theory Library for Pythoncited by 19
  6. 2021Can Transformer Language Models Predict Psychometric Properties?cited by 8
  7. 2024A Psychology-based Unified Dynamic Framework for Curriculum Learningcited by 6
  8. 2022Predicting Human Psychometric Properties Using Computational Language Modelscited by 5
  9. 2022Training Dynamics for Curriculum Learning: A Study on Monolingual and Cross-lingual NLUcited by 5
  10. 2023Ling-CL: Understanding NLP Models through Linguistic Curriculacited by 3
  11. 2023Complexity-Guided Curriculum Learning for Text Graphscited by 3
  12. 2024Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switchingcited by 3
9 more
  1. 2023Computational modeling of semantic changecited by 2
  2. 2023Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approachcited by 2
  3. 2023HuCurl: Human-induced Curriculum Discoverycited by 1
  4. 2024PATCH! {P}sychometrics-{A}ssis{T}ed Ben{CH}marking of Large Language Models against Human Populations: A Case Study of Proficiency in 8th Grade Mathematicscited by 1
  5. 2026Capacity-Dependent Effects of Data Selection for Reasoningcited by 1
  6. 2023H-COAL: Human Correction of AI-Generated Labels for Biomedical Named Entity Recognitioncited by 0
  7. 2024On Evaluation of Vision Datasets and Models using Human Competency Frameworkscited by 0
  8. 2026Learning to Solve Complex Problems via Dataset Decompositioncited by 0
  9. 2026Disentangling Curriculum Learning in NLP: Towards a Unifying Taxonomycited by 0