SMOL: Professionally translated parallel data for 115 under-represented languages

TL;DR

SMOL is a comprehensive open-source dataset of translated texts for 115 under-resourced languages, designed to improve machine translation and factuality assessment in low-resource settings.

Contribution

The paper introduces SMOL, a large multilingual dataset with sentence and document-level translations, including factuality annotations, for under-represented languages, enhancing translation and evaluation resources.

Findings

SMOL improves translation quality when used to prompt or fine-tune LLMs.

SMOL provides the first factuality datasets for many low-resource languages.

SMOL covers 125 language pairs with 6.1 million tokens.

Abstract

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.