mlsanityData to 7 Oct 2026

Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesis

arXiv 2411.01929 ↗First posted 4 Nov 2024cs.LG cs.AIcited by 0

On the map: Synthetic data and self-improvement for LLMs → LLM synthetic data generation

Abstract (arXiv)

Artificial Intelligence (AI) research often aims to develop models that can generalize reliably across complex datasets, yet this remains challenging in fields where data is scarce, intricate, or inaccessible. This paper introduces a novel approach that leverages three generative models of varying complexity to synthesize one of the most demanding structured datasets: Malicious Network Traffic. Our approach uniquely transforms numerical data into text, re-framing data generation as a language modeling task, which not only enhances data regularization but also significantly improves generalization and the quality of the synthetic data. Extensive statistical analyses demonstrate that our method surpasses state-of-the-art generative models in producing high-fidelity synthetic data. Additionally, we conduct a comprehensive study on synthetic data applications, effectiveness, and evaluation strategies, offering valuable insights into its role across various domains. Our code and pre-trained models are openly accessible at Github, enabling further exploration and application of our methodology. Index Terms: Data synthesis, machine learning, traffic generation, privacy preserving data, generative models.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 27 of its 56 references that are in the map. First posted on arXiv 4 Nov 2024.

Builds on

27 of 56 references.

References · 27

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  2. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  3. 2016Deep Learning with Differential Privacycited by 2,682
  4. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  5. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 329
  6. 2023Synthetic Data from Diffusion Models Improves ImageNet Classificationcited by 326
  7. 2017Data Augmentation Generative Adversarial Networkscited by 278
  8. 2018Differentially Private Generative Adversarial Networkcited by 235
  9. 2018Pros and Cons of GAN Evaluation Measurescited by 224
  10. 2018Adversarial Audio Synthesiscited by 222
  11. 2021Fake It Till You Make It: Face analysis in the wild using synthetic data alonecited by 200
  12. 2018Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approachcited by 119
15 more
  1. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
  2. 2017Differentially Private Mixture of Generative Neural Networkscited by 39
  3. 2021SynthASR: Unlocking Synthetic Data for Speech Recognitioncited by 34
  4. 2024A survey of synthetic data augmentation methods in computer visioncited by 34
  5. 2018Flow-based Network Traffic Generation using Generative Adversarial Networkscited by 26
  6. 2022MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labelscited by 24
  7. 2020Kamino: Constraint-Aware Differentially Private Data Synthesiscited by 23
  8. 2019Federated Generative Privacycited by 16
  9. 2023LDPTrace: Locally Differentially Private Trajectory Synthesiscited by 16
  10. 2024Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competitioncited by 16
  11. 2022PrivTrace: Differentially Private Trajectory Synthesis by Adaptive Markov Modelcited by 13
  12. 2017Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentationcited by 10
  13. 2019Unsupervised Neural Sensor Models for Synthetic LiDAR Data Augmentationcited by 8
  14. 2021Real-World Trajectory Sharing with Local Differential Privacycited by 7
  15. 2023The Use of Synthetic Data to Train AI Models: Opportunities and Risks for Sustainable Developmentcited by 6

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era
  2. 2024Best Practices and Lessons Learned on Synthetic Data
  3. 2024Generative AI for Synthetic Data Generation: Methods, Challenges and the Future
  4. 2024Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models
  5. 2025Synthetic Data Generation Using Large Language Models: Advances in Text and Code
  6. 2024Synthetic data: How could it be used for infectious disease research?
  7. 2026Lightweight GenAI for Network Traffic Generation: Fidelity, Augmentation, and Classification
  8. 2024On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey