Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh

Nurkhan Laiyk, Daniil Orel, Rituraj Joshi, Maiya Goloburda, Yuxia Wang, Preslav Nakov, Fajri Koto. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Authors

Daniil Orel (ORCID: https://orcid.org/0009-0007-5600-7032)
Preslav Nakov (ORCID: https://orcid.org/0000-0002-3600-1510)
Fajri Koto
Nurkhan Laiyk
Yuxia Wang (ORCID: https://orcid.org/0000-0002-6095-1030)
Rituraj Joshi
Maiya Goloburda

Publication Details

Published: 2025-01-01
DOI: https://doi.org/10.18653/v1/2025.acl-long.706
Citations: 1
Primary Topic: Multilingual Education and Policy
Type: article
Field-Weighted Citation Impact: 6.13

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article

Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh

Daniil Orel, Preslav Nakov, Fajri Koto, Nurkhan Laiyk et al.

Jan 1, 2025

1 citations

Multilingual Education and Policy

6.13

article

Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh

Daniil Orel, Preslav Nakov, Fajri Koto, Nurkhan Laiyk, Yuxia Wang, Rituraj Joshi, Maiya Goloburda

January 1, 2025article en

1 citations

Abstract

Openalex Percentile: Top 5%

Multilingual Education and Policy

6.13

DOI Open Access PDF OpenAlex

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Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh — Daniil Orel, Preslav Nakov, et al. · (2025) | TGRS Research Map | TGRS