DecorateLM: Data Engineering through Corpus Rating, Tagging, and Editing with Language Models

• URL: http://arxiv.org/abs/2410.05639v1
• Date: Tue, 8 Oct 2024 02:42:56 GMT
• Title: DecorateLM: Data Engineering through Corpus Rating, Tagging, and Editing with Language Models
• Authors: Ranchi Zhao, Zhen Leng Thai, Yifan Zhang, Shengding Hu, Yunqi Ba, Jie Zhou, Jie Cai, Zhiyuan Liu, Maosong Sun,
• Abstract summary: We introduce DecorateLM, a data engineering method designed to refine the pretraining corpus through data rating, tagging and editing. We then apply DecorateLM to enhance 100 billion tokens of the training corpus, selecting 45 billion tokens that exemplify high quality and diversity for the further training of another 1.2 billion parameter LLM. Our results demonstrate that employing such high-quality data can significantly boost model performance, showcasing a powerful approach to enhance the quality of the pretraining corpus.
• Score: 78.51470038301436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The performance of Large Language Models (LLMs) is substantially influenced by the pretraining corpus, which consists of vast quantities of unsupervised data processed by the models. Despite its critical role in model performance, ensuring the quality of this data is challenging due to its sheer volume and the absence of sample-level quality annotations and enhancements. In this paper, we introduce DecorateLM, a data engineering method designed to refine the pretraining corpus through data rating, tagging and editing. Specifically, DecorateLM rates texts against quality criteria, tags texts with hierarchical labels, and edits texts into a more formalized format. Due to the massive size of the pretraining corpus, adopting an LLM for decorating the entire corpus is less efficient. Therefore, to balance performance with efficiency, we curate a meticulously annotated training corpus for DecorateLM using a large language model and distill data engineering expertise into a compact 1.2 billion parameter small language model (SLM). We then apply DecorateLM to enhance 100 billion tokens of the training corpus, selecting 45 billion tokens that exemplify high quality and diversity for the further training of another 1.2 billion parameter LLM. Our results demonstrate that employing such high-quality data can significantly boost model performance, showcasing a powerful approach to enhance the quality of the pretraining corpus.

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