@inproceedings{fan-etal-2025-language,
title = "Language-Specific Layer Matters: Efficient Multilingual Enhancement for Large Vision-Language Models",
author = "Fan, Yuchun and
Wang, Yilin and
Mu, Yongyu and
Huang, Lei and
Li, Bei and
Feng, Xiaocheng and
Xiao, Tong and
Zhu, JingBo",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.666/",
pages = "12473--12500",
ISBN = "979-8-89176-335-7",
abstract = "Large vision-language models (LVLMs) have demonstrated exceptional capabilities in understanding visual information with human languages but also exhibit an imbalance in multilingual capabilities. In this work, we delve into the multilingual working pattern of LVLMs and identify a salient correlation between the multilingual understanding ability of LVLMs and language-specific neuron activations in shallow layers. Building on this insight, we introduce PLAST, a training recipe that achieves efficient multilingual enhancement for LVLMs by Precise LAnguage Specific layers fine-Tuning. PLAST first identifies layers involved in multilingual understanding by monitoring language-specific neuron activations. These layers are then precisely fine-tuned with question-translation pairs to achieve multilingual alignment. Our empirical results on MMBench and MMMB demonstrate that PLAST effectively improves the multilingual capabilities of LVLMs and achieves significant efficiency with only 14{\%} of the parameters tuned. Further analysis reveals that PLAST facilitates the language-specific visual information engagement in shallow layers."
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<abstract>Large vision-language models (LVLMs) have demonstrated exceptional capabilities in understanding visual information with human languages but also exhibit an imbalance in multilingual capabilities. In this work, we delve into the multilingual working pattern of LVLMs and identify a salient correlation between the multilingual understanding ability of LVLMs and language-specific neuron activations in shallow layers. Building on this insight, we introduce PLAST, a training recipe that achieves efficient multilingual enhancement for LVLMs by Precise LAnguage Specific layers fine-Tuning. PLAST first identifies layers involved in multilingual understanding by monitoring language-specific neuron activations. These layers are then precisely fine-tuned with question-translation pairs to achieve multilingual alignment. Our empirical results on MMBench and MMMB demonstrate that PLAST effectively improves the multilingual capabilities of LVLMs and achieves significant efficiency with only 14% of the parameters tuned. Further analysis reveals that PLAST facilitates the language-specific visual information engagement in shallow layers.</abstract>
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%0 Conference Proceedings
%T Language-Specific Layer Matters: Efficient Multilingual Enhancement for Large Vision-Language Models
%A Fan, Yuchun
%A Wang, Yilin
%A Mu, Yongyu
%A Huang, Lei
%A Li, Bei
%A Feng, Xiaocheng
%A Xiao, Tong
%A Zhu, JingBo
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F fan-etal-2025-language
%X Large vision-language models (LVLMs) have demonstrated exceptional capabilities in understanding visual information with human languages but also exhibit an imbalance in multilingual capabilities. In this work, we delve into the multilingual working pattern of LVLMs and identify a salient correlation between the multilingual understanding ability of LVLMs and language-specific neuron activations in shallow layers. Building on this insight, we introduce PLAST, a training recipe that achieves efficient multilingual enhancement for LVLMs by Precise LAnguage Specific layers fine-Tuning. PLAST first identifies layers involved in multilingual understanding by monitoring language-specific neuron activations. These layers are then precisely fine-tuned with question-translation pairs to achieve multilingual alignment. Our empirical results on MMBench and MMMB demonstrate that PLAST effectively improves the multilingual capabilities of LVLMs and achieves significant efficiency with only 14% of the parameters tuned. Further analysis reveals that PLAST facilitates the language-specific visual information engagement in shallow layers.
%U https://aclanthology.org/2025.findings-emnlp.666/
%P 12473-12500
Markdown (Informal)
[Language-Specific Layer Matters: Efficient Multilingual Enhancement for Large Vision-Language Models](https://aclanthology.org/2025.findings-emnlp.666/) (Fan et al., Findings 2025)
ACL
- Yuchun Fan, Yilin Wang, Yongyu Mu, Lei Huang, Bei Li, Xiaocheng Feng, Tong Xiao, and JingBo Zhu. 2025. Language-Specific Layer Matters: Efficient Multilingual Enhancement for Large Vision-Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 12473–12500, Suzhou, China. Association for Computational Linguistics.