DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis

2026-05-02

AI Summary

Existing synthetic data generation tools often suffer from complex workflows, inconsistent standards, and limited cross-modal extensibility, hindering their ability to meet the data demands of large language models in specialized domains and low-resource languages. This work proposes a configuration-driven, end-to-end open-source framework that standardizes multi-source data synthesis through a unified and controllable paradigm. Featuring a highly modular architecture, the framework flexibly adapts to diverse tasks and supports high-quality data generation across multiple pathways, modalities, and languages. Integrated with both a graphical user interface and command-line utilities, it significantly lowers the barrier to entry for users. Empirical evaluations demonstrate that the framework effectively balances generation efficiency and data quality across various scenarios, thereby accelerating the practical deployment of synthetic data in model training pipelines.

Abstract

Synthetic data has emerged as a crucial solution to the data scarcity bottleneck in large language models (LLMs), particularly for specialized domains and low-resource languages. However, the broader adoption of existing synthetic data tools is severely hindered by convoluted workflows, fragmented data standards, and limited scalability across modalities. To address these limitations, we develop DataArc-SynData-Toolkit, an open-source framework featuring: (1) a configuration-driven, end-to-end pipeline equipped with an intuitive visual interface and simplified CLI for exceptional usability; (2) a unified, quality-controllable synthesis paradigm that standardizes multi-source data generation to ensure high reusability; and (3) a highly modular architecture designed for seamless multimodal, multilingual, and multi-task adaptation. We apply the toolkit in multiple application scenarios. Experimental results demonstrate that our toolkit achieves an optimal balance between generation efficiency and data quality. By offering an end-to-end and visually interactive pipeline, DataArc-SynData-Toolkit significantly lowers the technical barrier to synthetic data generation and subsequent model training, accelerating its practical deployment in real-world applications.

Problem

• synthetic data
• data scarcity
• multimodal
• multilingual
• scalability

Innovation

• synthetic data
• multimodal
• multilingual
• modular architecture
• closed-loop framework