@inproceedings{gao-etal-2026-benchmarking-boosting,
title = "Benchmarking and Boosting Multilingual Capabilities of {LVLM}s via {OCR}-Centric Reinforcement Learning",
author = "Gao, Junyuan and
Song, Jiahe and
Wu, Jiang and
Zhu, Runchuan and
Shenguanlin and
Wang, Shasha and
Wei, Xingjian and
Yang, Haote and
Li, Weijia and
Wang, Bin and
Wu, Lijun and
He, Conghui",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.666/",
pages = "15335--15365",
ISBN = "979-8-89176-459-0",
abstract = "Evaluating the multilingual capabilities of Large Vision-Language Models (LVLMs) remains challenging because most benchmarks rely on non-parallel corpora, making it unclear whether cross-lingual performance gaps reflect model limitations or dataset inconsistencies. To address this, we introduce PM4Bench, the first multimodal, multilingual, multi-task benchmark built on a strictly parallel 10-language corpus, enabling fair, apples-to-apples cross-lingual comparison of model performance. We further introduce a vision setting that embeds textual inputs directly into images, better approximating deployment scenarios where LVLM-driven agents interact with virtual or physical environments through unified visual observations. Experiments with 10 LVLMs reveal that OCR is a key factor behind cross-lingual disparity when textual content is rendered visually. Motivated by this, we design an OCR-centric GRPO training strategy using fully synthesized, label-free OCR data, without expensive task-specific VQA supervision. The resulting model improves general multilingual VQA capability, reduces cross-lingual disparities under the vision setting, and transfers gains beyond PM4Bench. This methodology offers an efficient, label-free pathway toward more equitable multilingual deployment of LVLM-driven agents."
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<abstract>Evaluating the multilingual capabilities of Large Vision-Language Models (LVLMs) remains challenging because most benchmarks rely on non-parallel corpora, making it unclear whether cross-lingual performance gaps reflect model limitations or dataset inconsistencies. To address this, we introduce PM4Bench, the first multimodal, multilingual, multi-task benchmark built on a strictly parallel 10-language corpus, enabling fair, apples-to-apples cross-lingual comparison of model performance. We further introduce a vision setting that embeds textual inputs directly into images, better approximating deployment scenarios where LVLM-driven agents interact with virtual or physical environments through unified visual observations. Experiments with 10 LVLMs reveal that OCR is a key factor behind cross-lingual disparity when textual content is rendered visually. Motivated by this, we design an OCR-centric GRPO training strategy using fully synthesized, label-free OCR data, without expensive task-specific VQA supervision. The resulting model improves general multilingual VQA capability, reduces cross-lingual disparities under the vision setting, and transfers gains beyond PM4Bench. This methodology offers an efficient, label-free pathway toward more equitable multilingual deployment of LVLM-driven agents.</abstract>
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%0 Conference Proceedings
%T Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning
%A Gao, Junyuan
%A Song, Jiahe
%A Wu, Jiang
%A Zhu, Runchuan
%A Wang, Shasha
%A Wei, Xingjian
%A Yang, Haote
%A Li, Weijia
%A Wang, Bin
%A Wu, Lijun
%A He, Conghui
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%A Shenguanlin
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F gao-etal-2026-benchmarking-boosting
%X Evaluating the multilingual capabilities of Large Vision-Language Models (LVLMs) remains challenging because most benchmarks rely on non-parallel corpora, making it unclear whether cross-lingual performance gaps reflect model limitations or dataset inconsistencies. To address this, we introduce PM4Bench, the first multimodal, multilingual, multi-task benchmark built on a strictly parallel 10-language corpus, enabling fair, apples-to-apples cross-lingual comparison of model performance. We further introduce a vision setting that embeds textual inputs directly into images, better approximating deployment scenarios where LVLM-driven agents interact with virtual or physical environments through unified visual observations. Experiments with 10 LVLMs reveal that OCR is a key factor behind cross-lingual disparity when textual content is rendered visually. Motivated by this, we design an OCR-centric GRPO training strategy using fully synthesized, label-free OCR data, without expensive task-specific VQA supervision. The resulting model improves general multilingual VQA capability, reduces cross-lingual disparities under the vision setting, and transfers gains beyond PM4Bench. This methodology offers an efficient, label-free pathway toward more equitable multilingual deployment of LVLM-driven agents.
%U https://aclanthology.org/2026.emnlp-main.666/
%P 15335-15365
Markdown (Informal)
[Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning](https://aclanthology.org/2026.emnlp-main.666/) (Gao et al., EMNLP 2026)
ACL
- Junyuan Gao, Jiahe Song, Jiang Wu, Runchuan Zhu, Shenguanlin, Shasha Wang, Xingjian Wei, Haote Yang, Weijia Li, Bin Wang, Lijun Wu, and Conghui He. 2026. Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 15335–15365, Budapest, Hungary. Association for Computational Linguistics.