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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00130911" target="_blank" >RIV/00216224:14330/23:00130911 - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2023.bsnlp-1.12/" target="_blank" >https://aclanthology.org/2023.bsnlp-1.12/</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 9th Workshop on Slavic Natural Language Processing 2023 (SlavicNLP 2023)

ISBN

9781959429579

ISSN

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e-ISSN

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Number of pages

12

Pages from-to

94-105

Publisher name

Association for Computational Linguistics

Place of publication

Dubrovnik, Croatia

Event location

Dubrovnik, Croatia

Event date

May 2, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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