@inproceedings{soykan-sahin-2026-optimal,
title = "No Optimal Language Set Exists for Multilingual Instruction Tuning: Insights from a Linguistically-Informed Study",
author = {Soykan, G{\"u}rkan and
{\c{S}}ahin, G{\"o}zde},
editor = "Sedoc, Jo{\~a}o and
Shu, Raphael and
Drozd, Aleksandr and
Akula, Arjun and
Rogers, Anna and
Giorgi, Salvatore and
Benton, Adrian",
booktitle = "Proceedings of the Seventh Workshop on Insights from Negative Results in {NLP}",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.insights-1.12/",
pages = "95--101",
ISBN = "979-8-89176-480-4",
abstract = "Multilingual instruction tuning (MIT) is challenged by the curse of multilinguality, data scarcity, and high computational cost. A natural hypothesis is that carefully selecting a linguistically diverse set of languages yields universally better models. We test this systematically by evaluating linguistically-informed selection strategies{---}based on typological, geographical, semantic, and learned features{---}against random baselines across three model families (mGPT, mT5-xl, BLOOM) and five multilingual benchmarks. Our key negative finding is that no universal language selection strategy emerges in our fixed-budget setting: performance is strongly task- and model-dependent, and adding more languages beyond a modest threshold triggers degradation consistent with the curse of multilinguality. We discuss implications for MIT data curation and the pitfalls of benchmark-averaged evaluation. All resources are publicly available at {\textbackslash}url{\{}https://github.com/GGLAB-KU/ling-informed-mit{\}}."
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%0 Conference Proceedings
%T No Optimal Language Set Exists for Multilingual Instruction Tuning: Insights from a Linguistically-Informed Study
%A Soykan, Gürkan
%A Şahin, Gözde
%Y Sedoc, João
%Y Shu, Raphael
%Y Drozd, Aleksandr
%Y Akula, Arjun
%Y Rogers, Anna
%Y Giorgi, Salvatore
%Y Benton, Adrian
%S Proceedings of the Seventh Workshop on Insights from Negative Results in NLP
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-480-4
%F soykan-sahin-2026-optimal
%X Multilingual instruction tuning (MIT) is challenged by the curse of multilinguality, data scarcity, and high computational cost. A natural hypothesis is that carefully selecting a linguistically diverse set of languages yields universally better models. We test this systematically by evaluating linguistically-informed selection strategies—based on typological, geographical, semantic, and learned features—against random baselines across three model families (mGPT, mT5-xl, BLOOM) and five multilingual benchmarks. Our key negative finding is that no universal language selection strategy emerges in our fixed-budget setting: performance is strongly task- and model-dependent, and adding more languages beyond a modest threshold triggers degradation consistent with the curse of multilinguality. We discuss implications for MIT data curation and the pitfalls of benchmark-averaged evaluation. All resources are publicly available at \textbackslashurl{https://github.com/GGLAB-KU/ling-informed-mit}.
%U https://aclanthology.org/2026.insights-1.12/
%P 95-101Markdown (Informal)
[No Optimal Language Set Exists for Multilingual Instruction Tuning: Insights from a Linguistically-Informed Study](https://aclanthology.org/2026.insights-1.12/) (Soykan & Şahin, insights 2026)
ACL