Data-Juicer: A One-Stop Data Processing System for Large Language Models
Daoyuan Chen, Yilun Huang, Zhijian Ma, Hesen Chen, Xuchen Pan, Ce Ge, Dawei Gao, Yuexiang Xie, Zhaoyang Liu, Jinyang Gao, Yaliang Li, Bolin Ding, Jingren Zhou
TL;DR
Data-Juicer presents a comprehensive, open-source data-processing system tailored for large language models, addressing core challenges of data heterogeneity, evaluation cost, usability, and scale. It introduces a Standardized Operator Pool built on a unified data representation, enabling highly composable data recipes and a tight feedback loop through hyper-parameter optimization, visualization, and integrated LLM evaluation. Empirical results show Data-Juicer-refined pre-training and fine-tuning data improve HELM scores across benchmarks and GPT-4 pairwise wins while reducing data and compute requirements, plus substantial end-to-end efficiency gains and scalability. The work further demonstrates real-world applicability by underpinning Alibaba’s Tongyi products, highlighting practical impact and a path toward democratizing data-centric LLM development.
Abstract
The immense evolution in Large Language Models (LLMs) has underscored the importance of massive, heterogeneous, and high-quality data. A data recipe is a mixture of data from different sources for training LLMs, which plays a vital role in LLMs' performance. Existing open-source tools for LLM data processing are mostly tailored for specific data recipes. To continuously uncover the potential of LLMs, incorporate data from new sources, and improve LLMs' performance, we build a new system named Data-Juicer, with which we can efficiently generate diverse data recipes, explore different possibilities in forming data mixtures, and evaluate their effects on model performance. Different from traditional data-analytics pipelines, Data-Juicer faces some unique challenges. Firstly, the possible data sources for forming data recipes are truly heterogeneous and massive with various qualities. Secondly, it is extremely expensive to precisely evaluate data recipes' impact on LLMs' performance. Thirdly, the end users of Data-Juicer, model developers, need sufficient flexibility to configure and evaluate different data recipes. Data-Juicer features a fine-grained abstraction of pipelines for constructing data recipes, with over 50 built-in operators for easy composition and extension. By incorporating visualization and auto-evaluation capabilities, Data-Juicer enables a timely feedback loop for both LLM pre-training and fine-tuning. Further, Data-Juicer is optimized and integrated with ecosystems for LLM training, evaluation, and distributed computing. The data recipes derived with Data-Juicer gain notable improvements on state-of-the-art LLMs, by up to 7.45% increase in averaged score across 16 LLM benchmarks and 17.5% higher win rate in pair-wise GPT-4 evaluations. Our system, data recipes, and tutorials are released, calling for broader data-centric research on training and understanding LLMs.