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This study challenges the common assumption that linguistically diverse language sets universally improve multilingual models, revealing instead a "curse of multilinguality" where adding languages beyond a specific threshold degrades performance. The research demonstrates that optimal data selection is highly dependent on both the specific task and model architecture rather than following a single universal strategy for all scenarios. These findings offer critical guidance for researchers navigating resource constraints in large-scale instruction tuning efforts across multiple languages.
No Optimal Language Set Exists for Multilingual Instruction Tuning: Insights from a Linguistically-Informed Study
arXiv:2410.07809v2 Announce Type: replace 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 https://github.com/GGLAB-KU/ling-informed-mit.
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 this https URL.
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2410.07809 [cs.CL]
(or arXiv:2410.07809v2 [cs.CL] for this version)
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