mlsanityData to 7 Oct 2026

LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs?

arXiv 2408.16502 ↗First posted 29 Aug 2024cs.CLcited by 8

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Abstract (arXiv)

The generative large language models (LLMs) are increasingly being used for data augmentation tasks, where text samples are LLM-paraphrased and then used for classifier fine-tuning. However, a research that would confirm a clear cost-benefit advantage of LLMs over more established augmentation methods is largely missing. To study if (and when) is the LLM-based augmentation advantageous, we compared the effects of recent LLM augmentation methods with established ones on 6 datasets, 3 classifiers and 2 fine-tuning methods. We also varied the number of seeds and collected samples to better explore the downstream model accuracy space. Finally, we performed a cost-benefit analysis and show that LLM-based methods are worthy of deployment only when very small number of seeds is used. Moreover, in many cases, established methods lead to similar or better model accuracies.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 26 of its 42 references that are in the map. First posted on arXiv 29 Aug 2024.

Builds on

26 of 42 references.

References · 26

  1. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  2. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  3. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
  4. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
  5. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
  6. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  7. 2019Quantifying the Carbon Emissions of Machine Learningcited by 516
  8. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  9. 2022Is GPT-3 a Good Data Annotator?cited by 208
  10. 2018Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relationscited by 201
  11. 2020Reformulating Unsupervised Style Transfer as Paraphrase Generationcited by 181
  12. 2019Not Enough Data? Deep Learning to the Rescue!cited by 174
14 more
  1. 2018Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialogcited by 124
  2. 2018Conditional BERT Contextual Augmentationcited by 107
  3. 2023Finding Support Examples for In-Context Learningcited by 104
  4. 2023CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotationcited by 93
  5. 2021AEDA: An Easier Data Augmentation Technique for Text Classificationcited by 67
  6. 2022Data Curation Alone Can Stabilize In-context Learningcited by 58
  7. 2020Neural Syntactic Preordering for Controlled Paraphrase Generationcited by 56
  8. 2023ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustnesscited by 41
  9. 2020Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversitycited by 35
  10. 2023ZeroShotDataAug: Generating and Augmenting Training Data with ChatGPTcited by 32
  11. 2022Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuningcited by 25
  12. 2022MEAL: Stable and Active Learning for Few-Shot Promptingcited by 20
  13. 2023Using GPT-4 to Augment Unbalanced Data for Automatic Scoringcited by 20
  14. 2024Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentationcited by 17

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 3.

8 papers.

  1. 2024Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classificationcited by 7
  2. 2025Issue-Oriented Agent-Based Framework for Automated Review Comment Generationcited by 4
  3. 2025AI in Mental Health: Emotional and Sentiment Analysis of Large Language Models' Responses to Depression, Anxiety, and Stress Queriescited by 3
  4. 2025A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languagescited by 2
  5. 2025RoSE: Round-robin Synthetic Data Evaluation for Selecting LLM Generators without Human Test Setscited by 1
  6. 2026LLM-Augmented Therapy Normalization and Aspect-Based Sentiment Analysis for Treatment-Resistant Depression on Redditcited by 0
  7. 2026Class-Structure Preservation Beats Diversity: A Comprehensive Benchmark of Text Augmentation Methods for Imbalanced Text Classificationcited by 0
  8. 2026Geometric Filtering of LLM-Generated Samples for Few-Shot Text Classificationcited by 0