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DataEvolver addresses the critical bottleneck of manual data curation by introducing a system that automatically constructs and refines data preparation pipelines through multi-level self-evolution. This approach directly enhances downstream large language model performance, demonstrating that iterative co-evolution of training data and models can yield significant gains without human intervention. The research underscores a shift toward autonomous workflows that systematically improve dataset quality to support better LLM training outcomes.
DataEvolver: Automatic Data Preparation for Large Language Models through Multi-Level Self-Evolving
arXiv:2606.07001v3 Announce Type: replace-cross Abstract: High-quality training data is essential to large language models (LLMs) and typically requires extensive and costly manual curation. Automatic data preparation systems support reusable operators and task-driven workflows, yet adapting these workflows to the desired data quality remains challenging. High-quality examples provide a practical specification for guiding this adaptation. We introduce DataEvolver, a data preparation system that automatically constructs pipelines through multi-level self-evolution. At the operator level, it expands the operator library and refines logical plans to resolve dependency conflicts. At the pipeline level, it instantiates these plans into code and uses trial-output feedback to refine the pipeline toward seed-specified quality requirements. Experiments on seven benchmarks show that DataEvolver substantially improves data quality and achieves an average gain of approximately 10 score points in downstream LLM performance compared with training on original data, highlighting new opportunities for the iterative co-evolution of LLMs and data.
Abstract:High-quality training data is essential to large language models (LLMs) and typically requires extensive and costly manual curation. Automatic data preparation systems support reusable operators and task-driven workflows, yet adapting these workflows to the desired data quality remains challenging. High-quality examples provide a practical specification for guiding this adaptation. We introduce DataEvolver, a data preparation system that automatically constructs pipelines through multi-level self-evolution. At the operator level, it expands the operator library and refines logical plans to resolve dependency conflicts. At the pipeline level, it instantiates these plans into code and uses trial-output feedback to refine the pipeline toward seed-specified quality requirements. Experiments on seven benchmarks show that DataEvolver substantially improves data quality and achieves an average gain of approximately 10 score points in downstream LLM performance compared with training on original data, highlighting new opportunities for the iterative co-evolution of LLMs and data.
Databases (cs.DB); Artificial Intelligence (cs.AI)
arXiv:2606.07001 [cs.DB]
(or arXiv:2606.07001v3 [cs.DB] for this version)
https://doi.org/10.48550/arXiv.2606.07001 Focus to learn more arXiv-issued DOI via DataCite
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