DataFlow: An LLM-Driven Framework for Unified Data Preparation and Workflow Automation in the Era of Data-Centric AI

2025-12-18

AI Summary

The era of large language models (LLMs) faces critical challenges including insufficient high-quality data supply, fragmented data preparation pipelines, poor reproducibility, and lack of model-in-the-loop support. Method: We propose the first LLM-driven, unified data preparation framework for data-centric AI, featuring system-level abstractions and PyTorch-style APIs for modular design. We introduce DataFlow-Agent—the first agent that synthesizes executable data pipelines end-to-end from natural language specifications—and integrate LLM-powered operator synthesis, iterative validation, 200+ reusable operators, and six domain-agnostic pipeline templates. Results: Experiments on Text-to-SQL, code generation, and mathematical reasoning show our synthesized data significantly outperforms human-annotated and domain-specific synthetic data. Remarkably, just 10K samples surpass the performance of models trained on the million-scale Infinity-Instruct dataset, empirically validating the decisive impact of data quality on model performance.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

The rapidly growing demand for high-quality data in Large Language Models (LLMs) has intensified the need for scalable, reliable, and semantically rich data preparation pipelines. However, current practices remain dominated by ad-hoc scripts and loosely specified workflows, which lack principled abstractions, hinder reproducibility, and offer limited support for model-in-the-loop data generation. To address these challenges, we present DataFlow, a unified and extensible LLM-driven data preparation framework. DataFlow is designed with system-level abstractions that enable modular, reusable, and composable data transformations, and provides a PyTorch-style pipeline construction API for building debuggable and optimizable dataflows. The framework consists of nearly 200 reusable operators and six domain-general pipelines spanning text, mathematical reasoning, code, Text-to-SQL, agentic RAG, and large-scale knowledge extraction. To further improve usability, we introduce DataFlow-Agent, which automatically translates natural-language specifications into executable pipelines via operator synthesis, pipeline planning, and iterative verification. Across six representative use cases, DataFlow consistently improves downstream LLM performance. Our math, code, and text pipelines outperform curated human datasets and specialized synthetic baselines, achieving up to +3% execution accuracy in Text-to-SQL over SynSQL, +7% average improvements on code benchmarks, and 1--3 point gains on MATH, GSM8K, and AIME. Moreover, a unified 10K-sample dataset produced by DataFlow enables base models to surpass counterparts trained on 1M Infinity-Instruct data. These results demonstrate that DataFlow provides a practical and high-performance substrate for reliable, reproducible, and scalable LLM data preparation, and establishes a system-level foundation for future data-centric AI development.

Problem

• Addresses scalable, reliable data preparation for LLMs
• Replaces ad-hoc scripts with modular, reusable data transformations
• Automates pipeline creation from natural language specifications

Innovation

• LLM-driven framework for unified data preparation
• Modular and composable data transformations with PyTorch-style API
• Agent automatically converts natural language to executable pipelines