We introduce DataComp for Language Models (DCLM), a testbed for controlled dataset experiments with the goal of improving language models. As part of DCLM, we provide a standardized corpus of 240T tokens extracted from Common Crawl, effective pretraining recipes based on the OpenLM framework, and a broad suite of 53 downstream evaluations. Participants in the DCLM benchmark can experiment with data curation strategies such as deduplication, filtering, and data mixing at model scales ranging from 412M to 7B parameters. As a baseline for DCLM, we conduct extensive experiments and find that model-based filtering is key to assembling a high-quality training set. The resulting dataset, DCLM-Baseline enables training a 7B parameter language model from scratch to 64% 5-shot accuracy on MMLU with 2.6T training tokens. Compared to MAP-Neo, the previous state-of-the-art in open-data language models, DCLM-Baseline represents a 6.6 percentage point improvement on MMLU while being trained with 40% less compute. Our baseline model is also comparable to Mistral-7B-v0.3 and Llama 3 8B on MMLU (63% & 66%), and performs similarly on an average of 53 natural language understanding tasks while being trained with 6.6x less compute than Llama 3 8B. Our results highlight the importance of dataset design for training language models and offer a starting point for further research on data curation.

我们引入了用于语言模型的DataComp（DCLM）的测试平台，通过进行受控的数据集实验来改进语言模型。作为DCLM的一部分，我们提供了从Common Crawl中提取的标准化语料库，基于OpenLM框架的有效预训练方法以及广泛的53个下游评估。DCLM基准模型的实验结果表明，基于模型的过滤是组装高质量训练集的关键。我们的研究结果强调了训练语言模型的数据集设计的重要性，并为进一步研究数据策划提供了一个起点。

DataComp-LM: 寻找下一代语言模型训练集