DataEvolver: Automatic Data Preparation for Large Language Models through Multi-Level Self-Evolving

DataEvolver is a new self-evolving system that automatically prepares raw data for large language model training by constructing and refining data processing pipelines. The system achieves approximately 10% performance gains on downstream LLM tasks compared to using unprocessed data, reducing the need for expensive manual data curation.

DataEvolver addresses a critical bottleneck in LLM development: the labor-intensive process of preparing high-quality training data. Traditional approaches rely on static, predefined pipelines or manual human instructions, constraining their ability to adapt to varied data distributions. This research introduces an automated alternative that learns to construct optimal data preparation strategies through iterative refinement.

The system operates through a multi-level architecture that balances practical execution with pipeline effectiveness. At the operator level, it builds logical data transformation plans while managing dependencies. At the pipeline level, it converts these logical plans into executable code and continuously improves them through feedback loops that measure alignment between prepared data and high-quality reference examples. This hierarchical approach represents a meaningful advancement over static pipeline methods.

The reported 10% average performance improvement across seven benchmarks carries significant implications for AI development economics. If validated across diverse use cases, DataEvolver could substantially reduce the capital and human resources required to train competitive language models. This democratizes LLM development by making high-quality data preparation more accessible to organizations with limited annotation budgets.

Looking forward, the practical impact depends on whether DataEvolver generalizes beyond its tested benchmarks. Key questions include scalability to massive datasets, effectiveness across different model architectures, and whether the approach works for domain-specific applications beyond general-purpose LLMs. The research suggests a trend toward automating the entire ML pipeline, potentially shifting competitive advantages from data curation expertise to novel training methodologies.

• →DataEvolver automatically constructs data preparation pipelines, reducing manual curation requirements for LLM training
• →The system achieves 10% average performance improvements on downstream tasks by iteratively refining data quality
• →Multi-level architecture ensures both pipeline executability and effectiveness through operator and pipeline-level optimization
• →Automation of data preparation could lower barriers to entry for organizations building competitive language models
• →Results demonstrated across seven benchmarks suggest broad applicability of the self-evolving data approach