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.
| Comments: | 6 pages |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.08138 [cs.LG] |
| (or arXiv:2605.08138v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.08138 arXiv-issued DOI via DataCite |
From: Zhichao Shi [view email]
[v1]
Sat, 2 May 2026 05:37:58 UTC (2,627 KB)
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