SMOL : Professionally Translated Parallel Data for 115 Under-represented Languages (2025)

Abstract: We open-source SMOL (Set of Maximal Over-all Leverage), a suite of training data to un-lock machine translation for low-resource languages (LRLs). SMOL has been translated into123 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 source 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.

## introduction there exist no professionally-translated data for most of the world's 7000 or so languages, rendering tasks like machine translation near impossible. high-quality data is needed. however, it is 1 google/smol 2 experiments are mostly on a subset of 115 languages, before volunteer translations of additional languages finished. the paper title reflects this. in this work, we release the smol dataset, which provides professionally translated sentence-and document-level data for 123 lrls (125 language pairs). smol contains two sub-datasets: we demonstrate the utility of these data for finetuning and prompting llms for translation, and provide factuality annotations for all documents. ## related work there are not many training datasets with humantranslated data for low-resource languages (lrls), where we operationally define lrl as any language beyond the first 100 supported by most traditional crawls and mt providers (enumerated in appendix section a). tatoeba (tiedemann, 2020) is probably the most multilingual, but it is made of volunteer contributions and of unclear quality. the gatitos dataset @xcite consists of a 4000-entry lexicon translated into 170 lrls, but is only tokenlevel. most similar to the present work, nllb-seed is a high-quality, sentence-level training set of 6k sentences selected from english wikipedia and professionally translated into 44 lrls @xcite . there are also several professionallytranslated evaluation sets, namely @xcite , and ntrex @xcite . while highly multilingual, professionally translated training data is rare, there is a growing number of bottom-up community data sources organized through research collectives like masakhane @xcite , turkish interlingua @xcite , and ghananlp @xcite ; and conferences and workshops like africanlp, americasnlp @xcite and arabnlp. these datasets are usually generated by researchers fluent in the languages, and are therefore especially high quality. in addition to providing datasets, such efforts frequently also provide models and baselines, or even public interfaces, like the khaya translator web app foot_1 by ghananlp for west african languages, and the lesan.ai foot_2 translation website for ethiopian languages. participation is especially strong from the african continent, including corpora and models for pan-east-african languages @xcite , languages from the horn of africa @xcite , ethiopian languages @xcite , ugandan languages @xcite , emakhuwa @xcite , south-african languages @xcite , setswana and sepedi @xcite , yorùbá @xcite , oshiwambo @xcite , igbo @xcite , @xcite , twi @xcite , gbe (hacheme, 2021), bambara @xcite , and fon (emezue and dossou, 2020). outside of africa, corpora have been created for languages of the americas, including for four indigenous languages of peru in bustamante et al. (2020), the numerous results on the largely south-and central american languages from the first americasnlp conference @xcite , and the inuktitut language of canada @xcite . datasets for lower-resourced languages of india have also sprung up, including the 13-language pmindia (haddow and kirefu, 2020), and datasets focused on languages of the northeast like mizo @xcite , @xcite and @xcite . further west, parme @xcite has provided some of the first human-translated content for kurdish and iranian languages. finally, a variety of such datasets and models are available for public use on huggingface foot_3 or zenodo. 7 in addition to professionally translated data, there are also several web-crawled datasets for lrls, including madlad @xcite , oscar @xcite , glot500-c (imani et al., 2023), nllb @xcite , and the bloom library @xcite . ## text selection translation requires significant investment and can't be easily re-done, so great care needs be put into carefully choosing sentences to translate. for both sub-datasets smoldoc and smolsent, selection or generation of source text is done in english. selecting only english has clear biases, but also has advantages-most notably, for n languages, it requires n times less work to quality control. future work should consider focusing on non-english sources. ## data collection and verification several languages are contributed by volunteers; they are listed as co-authors. 10 for the other languages, the translation provider we contracted has worked with us for many years, and has a preexisting relationship with professional translators for all languages in the smol datasets. the translators are paid a fair wage, and their identities are contractually kept anonymous to us. we checked the delivery for duplicate translations, anomalous source/target length ratios, and similarity with google translate outputs. very few languages were flagged this way. following this, we ran funlangid @xcite on all segments and found no issues. manual inspection turned up several issues with nonunicode fonts (e.g. ô @xmath0, and nonstandard orthography for santali; these issues were then fixed. the choice of script, orthography and translation variety was challenging for many communities, including kurdish, zaza-gorani and gilaki languages, all of which have more than one orthography and lack a standard variety. the largest missing check is for fluency, which is hard to measure without trusted native speakers outside of the translation agency, or trusted llms; neither of which exist for all smol languages. ## finetuning and in-context learning we use fine-tuning and icl as tools to demonstrate the value of the smol dataset. as this is a data paper, these experiments are motivated by the maxim "what could any researcher simply train with public apis?" more involved techniques, e.g. reinforcement-learning-based approaches, will likely lead to stronger results. ## factuality review since smoldoc contains llm-generated sources, they contain some factual inaccuracies. we therefore do a full human audit and assign factuality codes to each document. each of the 584 documents was rated by three raters. each rating is accompanied by a detailed explanation, including sources cited. inter-annotator agreement was high, with cohen's κ between each pair of raters between 0.82-0.87. the error code distribution can be seen in figure 2 . the rubric is presented in all ratings and rationales are made available. in addition, each datum in smoldoc is given with a simple factuality annotation, which has the value has_errors if any one of the ratings was any of minor issues or clear issues, and ok otherwise. for some use-cases, like question-answering, practitioners may want to filter out nonfactual data; for others, like translation, one may not be troubled by factual errors. in addition to filtering, this also provides the first factuality dataset for most of these languages. ## conclusion we have open-sourced the smol dataset, a professionally-translated dataset covering 123 low resource languages and targeting the tasks of translation and factuality. it comprises smoldoc and smolsent, two training datasets with the complementary strengths of sentence selection (complex, and high token coverage) and document generation (contextual, varied domains, simpler sentences) respectively. we demonstrate that finetuning gemini 2.0 flash on these yields to substantial improvements in translation quality. smol joins a growing body of resources to support underserved languages in the age of ai.