Resources and Few-shot Learners for In-context Learning in Slavic Languages

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Publikace nespadá pod Ústav výpočetní techniky, ale pod Fakultu informatiky. Oficiální stránka publikace je na webu muni.cz.
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ŠTEFÁNIK Michal KADLČÍK Marek GRAMACKI Piotr SOJKA Petr

Rok publikování 2023
Druh Článek ve sborníku
Konference Proceedings of the 9th Workshop on Slavic Natural Language Processing 2023 (SlavicNLP 2023)
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www https://aclanthology.org/2023.bsnlp-1.12/
Klíčová slova in-context learning; low-resource; Slavic languages; datasets
Přiložené soubory
Popis Despite the rapid recent progress in creating accurate and compact in-context learners, most recent work focuses on in-context learning (ICL) for tasks in English. However, the ability to interact with users of languages outside English presents a great potential for broadening the applicability of language technologies to non-English speakers. In this work, we collect the infrastructure necessary for training and evaluation of ICL in a selection of Slavic languages: Czech, Polish, and Russian. We link a diverse set of datasets and cast these into a unified instructional format through a set of transformations and newly-crafted templates written purely in target languages. Using the newly-curated dataset, we evaluate a set of the most recent in-context learners and compare their results to the supervised baselines. Finally, we train, evaluate and publish a set of in-context learning models that we train on the collected resources and compare their performance to previous work. We find that ICL models tuned in English are also able to learn some tasks from non-English contexts, but multilingual instruction fine-tuning consistently improves the ICL ability. We also find that the massive multitask training can be outperformed by single-task training in the target language, uncovering the potential for specializing in-context learners to the language(s) of their application.
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