mlsanityData to 14 Oct 2026

Do Data-based Curricula Work?

arXiv 2112.06510 ↗First posted 13 Dec 2021cs.CLcited by 3

Not on the map. Why?

Abstract (arXiv)

Current state-of-the-art NLP systems use large neural networks that require lots of computational resources for training. Inspired by human knowledge acquisition, researchers have proposed curriculum learning, - sequencing of tasks (task-based curricula) or ordering and sampling of the datasets (data-based curricula) that facilitate training. This work investigates the benefits of data-based curriculum learning for large modern language models such as BERT and T5. We experiment with various curricula based on a range of complexity measures and different sampling strategies. Extensive experiments on different NLP tasks show that curricula based on various complexity measures rarely has any benefits while random sampling performs either as well or better than curricula.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 13 of its 26 references that are in the map. First posted on arXiv 13 Dec 2021.

Builds on

13 of 26 references.

References · 13

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  5. 2020Curriculum Learning for Reinforcement Learning Domains: A Framework and Surveycited by 373
  6. 2019Competence-based Curriculum Learning for Neural Machine Translationcited by 202
  7. 2020When Do Curricula Work?cited by 106
  8. 2017Curriculum Learning and Minibatch Bucketing in Neural Machine Translationcited by 63
  9. 2018An Empirical Exploration of Curriculum Learning for Neural Machine Translationcited by 58
  10. 2018A Fully Progressive Approach to Single-Image Super-Resolutioncited by 46
  11. 2016Criticality in Formal Languages and Statistical Physicscited by 35
  12. 2020Curriculum Learning with Diversity for Supervised Computer Vision Taskscited by 9
1 more
  1. 2020Text Complexity Classification Based on Linguistic Information: Application to Intelligent Tutoring of ESLcited by 3

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Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 1.

3 papers.

  1. 2023Baby Llama: knowledge distillation from an ensemble of teachers trained on a small dataset with no performance penaltycited by 57
  2. 2023How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biasescited by 23
  3. 2025DACL-RAG: Data Augmentation Strategy with Curriculum Learning for Retrieval-Augmented Generationcited by 1