mlsanityData to 14 Oct 2026

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

arXiv 2605.08138 ↗First posted 2 May 2026cs.LGcited by 1

On the map: Synthetic data and self-improvement for LLMs → LLM synthetic data generation

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 9 of its 17 references that are in the map. First posted on arXiv 2 May 2026.

Builds on

9 of 17 references.

References · 9

  1. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  2. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  3. 2024HybridFlow: A Flexible and Efficient RLHF Frameworkcited by 2,057
  4. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,356
  5. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,178
  6. 2024TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domainscited by 69
  7. 2025FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluationcited by 30
  8. 2024Targeted synthetic data generation for tabular data via hardness characterizationcited by 4
  9. 2025SearchInstruct: Enhancing Domain Adaptation via Retrieval-Based Instruction Dataset Creationcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026DataFoundry: Evolving Data Preparators via Recursive Self-Improvementcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2024A Survey on Data Synthesis and Augmentation for Large Language Models
  2. 2025Scaling Laws of Synthetic Data for Language Models
  3. 2025BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation
  4. 2024DataGen: Unified Synthetic Dataset Generation via Large Language Models
  5. 2026DataEvolver: Automatic Data Preparation for Large Language Models through Multi-Level Self-Evolving
  6. 2024DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
  7. 2024Better Synthetic Data by Retrieving and Transforming Existing Datasets
  8. 2025BhashaKritika: Building Synthetic Pretraining Data at Scale for Indic Languages