# A Survey on Data Augmentation in Large Model Era

arXiv: https://arxiv.org/abs/2401.15422 · HTML: https://mlsanity.com/paper/2401.15422/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 199 of its 285 references that are in the map.
- First posted on arXiv 27 Jan 2024.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 30 of 199)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)

## Built on by (most cited first, 30 of 31)

- [From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge](https://mlsanity.com/paper/2411.16594/) (2024)
- [A Survey on Data Synthesis and Augmentation for Large Language Models](https://mlsanity.com/paper/2410.12896/) (2024)
- [Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives](https://mlsanity.com/paper/2406.05615/) (2024)
- [NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli](https://mlsanity.com/paper/2405.02814/) (2024)
- [Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI](https://mlsanity.com/paper/2407.21523/) (2024)
- [Scaling Low-Resource MT via Synthetic Data Generation with LLMs](https://mlsanity.com/paper/2505.14423/) (2025)
- [How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?](https://mlsanity.com/paper/2502.14502/) (2025)
- [Lost in Execution: On the Multilingual Robustness of Tool Calling in Large Language Models](https://mlsanity.com/paper/2601.05366/) (2026)
- [A Survey of Data Synthesis Approaches](https://mlsanity.com/paper/2407.03672/) (2024)
- [A Survey on Efficient Large Language Model Training: From Data-centric Perspectives](https://mlsanity.com/paper/2510.25817/) (2025)
- [LLM-based Semantic Augmentation for Harmful Content Detection](https://mlsanity.com/paper/2504.15548/) (2025)
- [Debunk and Infer: Multimodal Fake News Detection via Diffusion-Generated Evidence and LLM Reasoning](https://mlsanity.com/paper/2506.21557/) (2025)
- [More Data or Better Data? A Critical Analysis of Data Selection and Synthesis for Mathematical Reasoning](https://mlsanity.com/paper/2510.07169/) (2025)
- [JurEE not Judges: safeguarding llm interactions with small, specialised Encoder Ensembles](https://mlsanity.com/paper/2410.08442/) (2024)
- [Enhancing weed detection performance by means of GenAI-based image augmentation](https://mlsanity.com/paper/2411.18513/) (2024)
- [A Multimodal Hybrid Late-Cascade Fusion Network for Enhanced 3D Object Detection](https://mlsanity.com/paper/2504.18419/) (2025)
- [Backtranslation and paraphrasing in the LLM era? Comparing data augmentation methods for emotion classification](https://mlsanity.com/paper/2507.14590/) (2025)
- [Margin Discrepancy-based Adversarial Training for Multi-Domain Text Classification](https://mlsanity.com/paper/2403.00888/) (2024)
- [You Only Need Half: Boosting Data Augmentation by Using Partial Content](https://mlsanity.com/paper/2405.02830/) (2024)
- [Stochastic Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/2406.00044/) (2024)
- [Seamless Optical Cloud Computing across Edge-Metro Network for Generative AI](https://mlsanity.com/paper/2412.12126/) (2024)
- [Assessing Data Augmentation-Induced Bias in Training and Testing of Machine Learning Models](https://mlsanity.com/paper/2502.01825/) (2025)
- [Evaluating Multimodal Large Language Models on Video Captioning via Monte Carlo Tree Search](https://mlsanity.com/paper/2506.11155/) (2025)
- [Towards Bridging Review Sparsity in Recommendation with Textual Edge Graph Representation](https://mlsanity.com/paper/2508.01128/) (2025)
- [Vision Transformer-based Adversarial Domain Adaptation](https://mlsanity.com/paper/2404.15817/) (2024)
- [OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning](https://mlsanity.com/paper/2506.17264/) (2025)
- [ProKG-Dial: Progressive Multi-Turn Dialogue Construction with Domain Knowledge Graphs](https://mlsanity.com/paper/2508.01869/) (2025)
- [Multi-turn Natural Language to Graph Query Language Translation](https://mlsanity.com/paper/2508.01871/) (2025)
- [Transplant Then Regenerate: A New Paradigm for Text Data Augmentation](https://mlsanity.com/paper/2508.14723/) (2025)
- [NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models](https://mlsanity.com/paper/2509.00378/) (2025)

## Abstract (arXiv)

Large models, encompassing large language and diffusion models, have shown exceptional promise in approximating human-level intelligence, garnering significant interest from both academic and industrial spheres. However, the training of these large models necessitates vast quantities of high-quality data, and with continuous updates to these models, the existing reservoir of high-quality data may soon be depleted. This challenge has catalyzed a surge in research focused on data augmentation methods. Leveraging large models, these data augmentation techniques have outperformed traditional approaches. This paper offers an exhaustive review of large model-driven data augmentation methods, adopting a comprehensive perspective. We begin by establishing a classification of relevant studies into three main categories: image augmentation, text augmentation, and paired data augmentation. Following this, we delve into various data post-processing techniques pertinent to large model-based data augmentation. Our discussion then expands to encompass the array of applications for these data augmentation methods within natural language processing, computer vision, and audio signal processing. We proceed to evaluate the successes and limitations of large model-based data augmentation across different scenarios. Concluding our review, we highlight prospective challenges and avenues for future exploration in the field of data augmentation. Our objective is to furnish researchers with critical insights, ultimately contributing to the advancement of more sophisticated large models. We consistently maintain the related open-source materials at: https://github.com/MLGroup-JLU/LLM-data-aug-survey.