We open-source SMOL (Set of Maximal Overall Leverage), a suite of training data to unlock machine translation for low-resource languages. SMOL has been translated into 124 (and growing) under-resourced languages (125 language pairs), including many for which there exist no previous public resources, for a total of 6.1M translated tokens. SMOL comprises two sub-datasets, each carefully chosen for maximum impact given its size: SMOLSENT, a set of sentences chosen for broad unique token coverage, and SMOLDOC, a document-level resource focusing on a broad topic coverage. They join the already released GATITOS for a trifecta of paragraph, sentence, and token-level content. We demonstrate that using SMOL to prompt or fine-tune Large Language Models yields robust chrF improvements. In addition to translation, we provide factuality ratings and rationales for all documents in SMOLDOC, yielding the first factuality datasets for most of these languages.
@article{arxiv.2502.12301,
title = {SMOL: Professionally translated parallel data for 115 under-represented languages},
author = {Isaac Caswell and Elizabeth Nielsen and Jiaming Luo and Colin Cherry and Geza Kovacs and Hadar Shemtov and Partha Talukdar and Dinesh Tewari and Baba Mamadi Diane and Djibrila Diane and Solo Farabado Cissé and Koulako Moussa Doumbouya and Edoardo Ferrante and Alessandro Guasoni and Christopher Homan and Mamadou K. Keita and Sudhamoy DebBarma and Ali Kuzhuget and David Anugraha and Muhammad Ravi Shulthan Habibi and Genta Indra Winata and Anthony Munthali and Sina Ahmadi and Andrei Chemyshev and Mingfei Lau and Jonathan Eng},
journal= {arXiv preprint arXiv:2502.12301},
year = {2025}
}