MADLAD-400: A Multilingual And Document-Level Large Audited Dataset

Published: 26 Sept 2023, Last Modified: 02 Nov 2023NeurIPS 2023 Datasets and Benchmarks PosterEveryoneRevisionsBibTeX
Keywords: CommonCrawl, Low Resource Languages, Underrepresented Languages, Multilinguality, LLMs, Large Language Models, Machine Translation
TL;DR: MADLAD-400 is a manually audited general domain monolingual dataset based on CommonCrawl spanning 419 languages.
Abstract: We introduce MADLAD-400, a manually audited, general domain 3T token monolingual dataset based on CommonCrawl, spanning 419 languages. We discuss the limitations revealed by self-auditing MADLAD-400, and the role data auditing had in the dataset creation process. We then train and release a 10.7B-parameter multilingual machine translation model on 250 billion tokens covering over 450 languages using publicly available data, and find that it is competitive with models that are significantly larger, and report the results on different domains. In addition, we train a 8B-parameter language model, and assess the results on few-shot translation. We make the baseline models available to the research community.
Supplementary Material: pdf
Submission Number: 409