# Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesis

arXiv: https://arxiv.org/abs/2411.01929 · HTML: https://mlsanity.com/paper/2411.01929/

## Key facts

- No citing papers in the map yet (data through 14 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.

## Placed in

- Synthetic data and self-improvement for LLMs / [LLM synthetic data generation](https://mlsanity.com/synth/llm-synthetic-data-generation/)

## Builds on (most cited first, 27 of 27)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [Synthetic Data from Diffusion Models Improves ImageNet Classification](https://mlsanity.com/paper/2304.08466/) (2023)
- [Data Augmentation Generative Adversarial Networks](https://mlsanity.com/paper/1711.04340/) (2017)
- [Differentially Private Generative Adversarial Network](https://mlsanity.com/paper/1802.06739/) (2018)
- [Adversarial Audio Synthesis](https://mlsanity.com/paper/1802.04208/) (2018)
- [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018)
- [Fake It Till You Make It: Face analysis in the wild using synthetic data alone](https://mlsanity.com/paper/2109.15102/) (2021)
- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach](https://mlsanity.com/paper/1812.05040/) (2018)
- [Differentially Private Mixture of Generative Neural Networks](https://mlsanity.com/paper/1709.04514/) (2017)
- [SynthASR: Unlocking Synthetic Data for Speech Recognition](https://mlsanity.com/paper/2106.07803/) (2021)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [Flow-based Network Traffic Generation using Generative Adversarial Networks](https://mlsanity.com/paper/1810.07795/) (2018)
- [Kamino: Constraint-Aware Differentially Private Data Synthesis](https://mlsanity.com/paper/2012.15713/) (2020)
- [MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels](https://mlsanity.com/paper/2203.12454/) (2022)
- [Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition](https://mlsanity.com/paper/2404.08008/) (2024)
- [Federated Generative Privacy](https://mlsanity.com/paper/1910.08385/) (2019)
- [LDPTrace: Locally Differentially Private Trajectory Synthesis](https://mlsanity.com/paper/2302.06180/) (2023)
- [PrivTrace: Differentially Private Trajectory Synthesis by Adaptive Markov Model](https://mlsanity.com/paper/2210.00581/) (2022)
- [Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation](https://mlsanity.com/paper/1711.06969/) (2017)
- [Unsupervised Neural Sensor Models for Synthetic LiDAR Data Augmentation](https://mlsanity.com/paper/1911.10575/) (2019)
- [Real-World Trajectory Sharing with Local Differential Privacy](https://mlsanity.com/paper/2108.02084/) (2021)
- [The Use of Synthetic Data to Train AI Models: Opportunities and Risks for Sustainable Development](https://mlsanity.com/paper/2309.00652/) (2023)

## 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.