DataComp-LM: In search of the next generation of training sets for
  language models
Authors: name: Jeffrey Li, name: Alex Fang, name: Georgios Smyrnis, name: Maor Ivgi, name: Matt Jordan, name: Samir Gadre, name: Hritik Bansal, name: Etash Guha, name: Sedrick Keh, name: Kushal Arora, name: Saurabh Garg, name: Rui Xin, name: Niklas Muennighoff, name: Reinhard Heckel, name: Jean Mercat, name: Mayee Chen, name: Suchin Gururangan, name: Mitchell Wortsman, name: Alon Albalak, name: Yonatan Bitton, name: Marianna Nezhurina, name: Amro Abbas, name: Cheng-Yu Hsieh, name: Dhruba Ghosh, name: Josh Gardner, name: Maciej Kilian, name: Hanlin Zhang, name: Rulin Shao, name: Sarah Pratt, name: Sunny Sanyal, name: Gabriel Ilharco, name: Giannis Daras, name: Kalyani Marathe, name: Aaron Gokaslan, name: Jieyu Zhang, name: Khyathi Chandu, name: Thao Nguyen, name: Igor Vasiljevic, name: Sham Kakade, name: Shuran Song, name: Sujay Sanghavi, name: Fartash Faghri, name: Sewoong Oh, name: Luke Zettlemoyer, name: Kyle Lo, name: Alaaeldin El-Nouby, name: Hadi Pouransari, name: Alexander Toshev, name: Stephanie Wang, name: Dirk Groeneveld, name: Luca Soldani, name: Pang Wei Koh, name: Jenia Jitsev, name: Thomas Kollar, name: Alexandros G. Dimakis, name: Yair Carmon, name: Achal Dave, name: Ludwig Schmidt, name: Vaishaal Shankar

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.