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arXiv · 2311.12537

Oasis: Data Curation and Assessment System for Pretraining of Large Language Models

Tong Zhou·Yubo Chen·Pengfei Cao·Kang Liu·Jun Zhao·Shengping Liu
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Abstract

Data is one of the most critical elements in building a large language model. However, existing systems either fail to customize a corpus curation pipeline or neglect to leverage comprehensive corpus assessment for iterative optimization of the curation. To this end, we present a pretraining corpus curation and assessment platform called Oasis -- a one-stop system for data quality improvement and quantification with user-friendly interactive interfaces. Specifically, the interactive modular rule filter module can devise customized rules according to explicit feedback. The debiased neural filter module builds the quality classification dataset in a negative-centric manner to remove the undesired bias. The adaptive document deduplication module could execute large-scale deduplication with limited memory resources. These three parts constitute the customized data curation module. And in the holistic data assessment module, a corpus can be assessed in local and global views, with three evaluation means including human, GPT-4, and heuristic metrics. We exhibit a complete process to use Oasis for the curation and assessment of pretraining data. In addition, an 800GB bilingual corpus curated by Oasis is publicly released.

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Tong Zhou, Yubo Chen, Pengfei Cao, Kang Liu, Jun Zhao, Shengping Liu. 2023-11-21. Oasis: Data Curation and Assessment System for Pretraining of Large Language Models. https://arxiv.org/abs/2311.12537

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