# On the use of LLMs to generate a dataset of Neural Networks

arXiv: https://arxiv.org/abs/2602.04388 · HTML: https://mlsanity.com/paper/2602.04388/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 4 Feb 2026.

## Builds on (most cited first, 20 of 20)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [NAS-Bench-101: Towards Reproducible Neural Architecture Search](https://mlsanity.com/paper/1902.09635/) (2019)
- [Safety Verification of Deep Neural Networks](https://mlsanity.com/paper/1610.06940/) (2016)
- [On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://mlsanity.com/paper/2406.15126/) (2024)
- [EvoPrompting: Language Models for Code-Level Neural Architecture Search](https://mlsanity.com/paper/2302.14838/) (2023)
- [LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity Optimization](https://mlsanity.com/paper/2306.01102/) (2023)
- [Synthetic Data Generation Using Large Language Models: Advances in Text and Code](https://mlsanity.com/paper/2503.14023/) (2025)
- [Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization](https://mlsanity.com/paper/1905.04970/) (2019)
- [LLM Performance Predictors are good initializers for Architecture Search](https://mlsanity.com/paper/2310.16712/) (2023)
- [Position: Leverage Foundational Models for Black-Box Optimization](https://mlsanity.com/paper/2405.03547/) (2024)
- [HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection](https://mlsanity.com/paper/2408.02927/) (2024)
- [NAS-HPO-Bench-II: A Benchmark Dataset on Joint Optimization of Convolutional Neural Network Architecture and Training Hyperparameters](https://mlsanity.com/paper/2110.10165/) (2021)
- [Automated Verification of Neural Networks: Advances, Challenges and Perspectives](https://mlsanity.com/paper/1805.09938/) (2018)
- [Refactoring Deep Learning Code: A Study of Practices and Unsatisfied Tool Needs](https://mlsanity.com/paper/2405.04861/) (2024)
- [Younger: The First Dataset for Artificial Intelligence-Generated Neural Network Architecture](https://mlsanity.com/paper/2406.15132/) (2024)
- [Instruction-Guided Autoregressive Neural Network Parameter Generation](https://mlsanity.com/paper/2504.02012/) (2025)
- [TAGAL: Tabular Data Generation using Agentic LLM Methods](https://mlsanity.com/paper/2509.04152/) (2025)
- [NeuroGen: Neural Network Parameter Generation via Large Language Models](https://mlsanity.com/paper/2505.12470/) (2025)
- [Towards Migrating Neural Network Implementations](https://mlsanity.com/paper/2511.02610/) (2025)

## Abstract (arXiv)

Neural networks are increasingly used to support decision-making. To verify their reliability and adaptability, researchers and practitioners have proposed a variety of tools and methods for tasks such as NN code verification, refactoring, and migration. These tools play a crucial role in guaranteeing both the correctness and maintainability of neural network architectures, helping to prevent implementation errors, simplify model updates, and ensure that complex networks can be reliably extended and reused. Yet, assessing their effectiveness remains challenging due to the lack of publicly diverse datasets of neural networks that would allow systematic evaluation. To address this gap, we leverage large language models (LLMs) to automatically generate a dataset of neural networks that can serve as a benchmark for validation. The dataset is designed to cover diverse architectural components and to handle multiple input data types and tasks. In total, 608 samples are generated, each conforming to a set of precise design choices. To further ensure their consistency, we validate the correctness of the generated networks using static analysis and symbolic tracing. We make the dataset publicly available to support the community in advancing research on neural network reliability and adaptability.