Problem
Research questions and friction points this paper is trying to address.
Multimodal Synthetic Data Generation
Dataset Diversity
Iterative Process
NeMo Data Designer: An Extensible Framework for Multimodal Synthetic Data Generation
🤖 AI Summary
本文介绍NeMo数据设计器，一种用于生成多模态合成数据的开源框架，通过声明式配置和插件系统提高数据集多样性和工作流可重复性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
multimodal synthetic data generation
declarative configuration
plugin system
preview-and-revision loop
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