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    <abstract>Multilingual language models (MLLMs) are crucial for handling text across various languages, yet they often show performance disparities due to differences in resource availability and linguistic characteristics. While the impact of pre-train data percentage and model size on performance is well-known, our study reveals additional critical factors that significantly influence MLLM effectiveness. Analyzing a wide range of features, including geographical, linguistic, and resource-related aspects, we focus on the SIB-200 dataset for classification and the Flores-200 dataset for machine translation, using regression models and SHAP values across 204 languages. Our findings identify token similarity and country similarity as pivotal factors, alongside pre-train data and model size, in enhancing model performance. Token similarity facilitates cross-lingual transfer, while country similarity highlights the importance of shared cultural and linguistic contexts. These insights offer valuable guidance for developing more equitable and effective multilingual language models, particularly for underrepresented languages.</abstract>
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%0 Conference Proceedings
%T Beyond Data Quantity: Key Factors Driving Performance in Multilingual Language Models
%A Bagheri Nezhad, Sina
%A Agrawal, Ameeta
%A Pokharel, Rhitabrat
%Y Hettiarachchi, Hansi
%Y Ranasinghe, Tharindu
%Y Rayson, Paul
%Y Mitkov, Ruslan
%Y Gaber, Mohamed
%Y Premasiri, Damith
%Y Tan, Fiona Anting
%Y Uyangodage, Lasitha
%S Proceedings of the First Workshop on Language Models for Low-Resource Languages
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F bagheri-nezhad-etal-2025-beyond
%X Multilingual language models (MLLMs) are crucial for handling text across various languages, yet they often show performance disparities due to differences in resource availability and linguistic characteristics. While the impact of pre-train data percentage and model size on performance is well-known, our study reveals additional critical factors that significantly influence MLLM effectiveness. Analyzing a wide range of features, including geographical, linguistic, and resource-related aspects, we focus on the SIB-200 dataset for classification and the Flores-200 dataset for machine translation, using regression models and SHAP values across 204 languages. Our findings identify token similarity and country similarity as pivotal factors, alongside pre-train data and model size, in enhancing model performance. Token similarity facilitates cross-lingual transfer, while country similarity highlights the importance of shared cultural and linguistic contexts. These insights offer valuable guidance for developing more equitable and effective multilingual language models, particularly for underrepresented languages.
%U https://aclanthology.org/2025.loreslm-1.18/
%P 225-239

Markdown (Informal)

[Beyond Data Quantity: Key Factors Driving Performance in Multilingual Language Models](https://aclanthology.org/2025.loreslm-1.18/) (Bagheri Nezhad et al., LoResLM 2025)

• Beyond Data Quantity: Key Factors Driving Performance in Multilingual Language Models (Bagheri Nezhad et al., LoResLM 2025)

ACL

• Sina Bagheri Nezhad, Ameeta Agrawal, and Rhitabrat Pokharel. 2025. Beyond Data Quantity: Key Factors Driving Performance in Multilingual Language Models. In Proceedings of the First Workshop on Language Models for Low-Resource Languages, pages 225–239, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.