让数据自动进化，提升大模型训练质量
DataEvolver: Automatic Data Preparation for Large Language Models through Multi-Level Self-Evolving
- 通过多层级自演化机制，自动构建数据处理流程
- 在7个基准上使下游模型性能平均提升10%
- 适合需要高质量数据的LLM研发团队使用
高质量训练数据对大语言模型至关重要，但通常需耗费大量人力进行手工筛选。现有自动数据准备方法依赖预设流程或人工指令，难以适应多样数据分布，且缺乏高质量样本的系统性指导。本文提出DataEvolver，首个自演化数据准备系统，能自动将原始数据转化为高质量数据。其采用多层级机制：在操作符层面，逐步扩展操作集并解决依赖冲突；在流程层面，将逻辑计划转为可执行代码，并通过反馈循环迭代优化流程编排，缩小生成数据与高质量样本之间的分布差距。在7个基准上的实验表明，与原始数据训练相比，DataEvolver显著提升数据质量，平均使下游大模型性能提升10%，揭示了大模型与数据协同进化的全新可能。
原文摘要 · Abstract (English)
High-quality training data is essential to large language models (LLMs) and typically requires extensive and costly manual curation. Existing automatic data preparation methods rely on predefined pipelines or customized human instructions, which limits their adaptability to diverse data distributions and lacks principled guidance from high-quality examples. In this paper, we introduce DataEvolver, the first self-evolving data preparation system that automatically constructs pipelines to transform raw data into high-quality data. DataEvolver employs a multi-level mechanism to ensure both pipeline executability and effectiveness. At the operator level, it incrementally expands the operator set to construct a logical plan while resolving dependency conflicts. At the pipeline level, it instantiates logical plans into executable code and iteratively refines pipeline orchestration through a feedback loop that reduces the distribution gap between prepared data and high-quality examples. Experiments on seven benchmarks show that DataEvolver substantially improves data quality and achieves an average 10\% gain in downstream LLM performance compared with training on original data, highlighting new opportunities for the iterative co-evolution of LLMs and data.
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