Language Dominance in Multilingual Large Language Models

Nadav Shani, Ali Basirat


Abstract
This paper investigates the language dominance hypothesis in multilingual large language models (LLMs), which posits that cross-lingual understanding is facilitated by an implicit translation into a dominant language seen more frequently during pretraining. We propose a novel approach to quantify how languages influence one another in a language model. By analyzing the hidden states across intermediate layers of language models, we model interactions between language-specific embedding spaces using Gaussian Mixture Models. Our results reveal only weak signs of language dominance in middle layers, affecting only a fraction of tokens. Our findings suggest that multilingual processing in LLMs is better explained by language-specific and shared representational spaces rather than internal translation into a single dominant language.
Anthology ID:
2025.blackboxnlp-1.7
Volume:
Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP
Month:
November
Year:
2025
Address:
Suzhou, China
Editors:
Yonatan Belinkov, Aaron Mueller, Najoung Kim, Hosein Mohebbi, Hanjie Chen, Dana Arad, Gabriele Sarti
Venues:
BlackboxNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
137–148
Language:
URL:
https://preview.aclanthology.org/ingest-emnlp/2025.blackboxnlp-1.7/
DOI:
Bibkey:
Cite (ACL):
Nadav Shani and Ali Basirat. 2025. Language Dominance in Multilingual Large Language Models. In Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pages 137–148, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Language Dominance in Multilingual Large Language Models (Shani & Basirat, BlackboxNLP 2025)
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PDF:
https://preview.aclanthology.org/ingest-emnlp/2025.blackboxnlp-1.7.pdf