arXiv cs.AI· Chao Deng, Shaolei Zhang, Ju Fan, Xiaoyong Du·· 3 天前AI 评分27
DataEvolver：通过多级自进化实现大语言模型自动数据准备
DataEvolver: Automatic Data Preparation for Large Language Models through Multi-Level Self-Evolving
AI 导读
DataEvolver 是一个通过多级自进化自动构建数据处理流水线的数据准备系统，在算子层面扩展算子库并优化逻辑计划以解决依赖冲突，在流水线层面将计划实例化为代码并利用试运行输出反馈迭代优化。在七个基准上，其数据质量显著提升，下游 LLM 性能相比原始数据训练平均提升约 10 分。
正文 · 原文
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.
| Subjects: | Databases (cs.DB); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.07001 [cs.DB] |
| (or arXiv:2606.07001v3 [cs.DB] for this version) | |
| https://doi.org/10.48550/arXiv.2606.07001 arXiv-issued DOI via DataCite |
Submission history
From: Chao Deng [view email]
[v1]
Fri, 5 Jun 2026 07:44:34 UTC (4,148 KB)
[v2]
Wed, 10 Jun 2026 07:54:44 UTC (4,148 KB)
[v3]
Tue, 13 Oct 2026 06:09:34 UTC (4,149 KB)
来源：arXiv cs.AI · arxiv.org