NeMo Data Designer：一个用于多模态合成数据生成的可扩展框架
NeMo Data Designer: An Extensible Framework for Multimodal Synthetic Data Generation
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中文总结 AI 辅助
本文提出NeMo Data Designer（NDD），一个开源、可扩展的多模态合成数据生成框架，通过声明式配置、插件系统和预览-修订循环，支持多样化数据集的迭代生成，并在多个领域任务中验证了其有效性。
中文摘要 AI 辅助
我们提出了NeMo Data Designer（NDD），一个开源的、通用的多模态合成数据生成（SDG）框架。NDD设计为易于使用，提供了一种声明式配置格式，人类和/或智能体用户可在其中定义每个数据集列，列类型涵盖文本、代码、结构化输出、图像、嵌入以及统计采样器，这些采样器被显式配置以引导数据集多样性。通过该框架灵活的插件系统，可以引入额外的列类型和功能。NDD的配置是一个可检查的工件，支持工作流共享和可复现性。SDG本质上是一个迭代过程。因此，NDD在其核心工作流中构建了预览-修订循环，允许用户生成并检查少量记录，细化规范，然后以全规模重新运行生成。在运行时，NDD解析依赖关系，调度对用户提供的模型端点的调用，并重试失败的请求。我们描述了NDD的架构和编程模型，并展示了涵盖结构化、智能体、多模态和领域专业化任务（包括用于Nemotron模型开发和生产企业部署的数据集）的案例研究。
英文摘要
We present NeMo Data Designer (NDD), an open-source, general-purpose framework for multi-modal synthetic data generation (SDG). Designed to be intuitive to use, NDD provides a declarative configuration format in which human and/or agent users define each dataset column, with column types spanning text, code, structured outputs, images, embeddings, and statistical samplers that are explicitly configured to steer dataset diversity. Additional column types and functionality can be introduced using the framework's flexible plugin system. NDD's configuration is an inspectable artifact, supporting workflow sharing and reproducibility. SDG is an inherently iterative process. NDD therefore builds a preview-and-revision loop into its core workflow, allowing users to generate and inspect a small number of records, refine the specification, and rerun generation at full scale. At runtime, NDD resolves dependencies, schedules calls to user-provided model endpoints, and retries failed requests. We describe NDD's architecture and programming model and present case studies spanning structured, agentic, multimodal, and domain-specialized tasks, including datasets used in Nemotron model development and in production enterprise deployments.
