用大模型生成608个神经网络，助力可靠性验证研究
On the use of LLMs to generate a dataset of Neural Networks
- 用LLM自动构造多样化神经网络架构
- 生成608个符合精确设计规范的样本
- 适合做模型验证与可维护性研究的开发者
神经网络在决策支持中日益普及。为验证其可靠性和可适应性，研究者提出了多种工具和方法，用于神经网络代码验证、重构与迁移。这些工具对确保模型正确性与可维护性至关重要，有助于避免实现错误、简化模型更新，并保障复杂网络的可靠扩展与复用。然而，由于缺乏公开且多样化的神经网络数据集，评估这些工具的有效性仍具挑战。为此，我们利用大语言模型（LLMs）自动生成一个神经网络数据集，作为验证基准。该数据集涵盖多种架构组件，支持多类输入数据与任务。共生成608个样本，每个均符合一组精确的设计选择。为确保一致性，我们通过静态分析与符号追踪验证生成网络的正确性。数据集已公开，以支持社区在神经网络可靠性与可适应性研究方面的进展。
原文摘要 · Abstract (English)
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.
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