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Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges

Paper · 2024 · Bosheng Ding, Chengwei Qin, Ruochen Zhao et al.

Summary

This survey examines how large language models have changed data augmentation (DA): expanding training data without collecting new examples. The authors argue the topic is urgent because high-quality human-written text is becoming scarce relative to the data appetite of ever-larger models. LLM-produced synthetic data can fill that gap, and in some cases it rivals or beats human-curated data. The paper introduces no new dataset or model. Its contribution is a structured taxonomy of existing work, viewed from two angles:

  • Data: how synthetic examples are obtained.
  • Learning: how generated data feeds into different training regimes, including LLMs being trained on output from other LLMs.

An appendix catalogues methods by task and by domain. The authors also identify open problems around reliability, control, cultural fit, modality coverage and privacy. They note limitations of LLM-based DA in general: inherited bias, weak output in niche domains, high compute cost and repetitive generations.

For a team building NLP resources for a less-resourced language such as Ukrainian, the paper works as a map for choosing an augmentation strategy and anticipating its risks.

Key points

  • Four data-side categories: data creation (few-shot generation of new examples), data labeling (LLM annotates existing corpora), data reformation (rewriting existing samples into new ones), and co-annotation (humans and LLMs labeling jointly).
  • Two learning-side families:
    • Generative: supervised instruction learning, in-context learning and alignment learning.
    • Discriminative: pseudo-labeled data for classification and LLM-assigned scores for regression.
  • Five challenge areas: data contamination, controllable DA, culture-aware DA, multimodal DA (images, audio, video, graphs) and privacy-preserving DA. For privacy, the authors point to differential privacy and federated learning as possible remedies.
  • Illustrative result: Chintagunta et al. (2021) used GPT-3 to synthesize medical dialogue summaries. Mixing these with a small human-labeled set matched results that would otherwise require about 30 times more human annotation.
  • Motivating forecast: citing Villalobos et al. (2022), the authors note that the stock of high-quality language data could be exhausted around 2026.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Princeton UniversityPrinceton · United States137 works · 53,849 citations🏢SalesforceSan Francisco · United States63 works · 18,348 citations🎓Nanyang Technological UniversitySingapore · Singapore193 works · 27,739 citations🎓University of Wisconsin–MadisonMadison · United States60 works · 49,213 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 19,503 worksArtificial Intelligence · 18,805 worksArts and Humanities · 17,959 worksLanguage and Linguistics · 17,789 works

Cite

Cite as Bosheng Ding, Chengwei Qin, Ruochen Zhao, Tianze Luo, Xinze Li, Guizhen Chen, Wenhan Xia, Junjie Hu, Anh Tuan Luu and Shafiq Joty. 2024. Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges. arXiv:2403.02990.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2403.02990 · arXiv 2403.02990

@misc{ding2024data,
  title = {{Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges}},
  author = {Bosheng Ding, Chengwei Qin, Ruochen Zhao, Tianze Luo, Xinze Li, Guizhen Chen, Wenhan Xia, Junjie Hu, Anh Tuan Luu and Shafiq Joty},
  year = {2024},
  doi = {10.48550/arxiv.2403.02990},
  eprint = {2403.02990},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2403.02990}
}

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