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        <title>What Is Missing in Multilingual Visual Reasoning and How to Fix It</title>
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        <namePart type="given">Yueqi</namePart>
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    <abstract>NLP models today strive for supporting multiple languages and modalities, improving accessibility for diverse users. In this paper, we evaluate their multilingual, multimodal capabilities by testing on a visual reasoning task. We observe that proprietary systems like GPT-4V obtain the best performance on this task now, but open models lag in comparison. Surprisingly, GPT-4V exhibits similar performance between English and other languages, indicating the potential for equitable system development across languages. Our analysis on model failures reveals three key aspects that make this task challenging: multilinguality, complex reasoning, and multimodality. To address these challenges, we propose three targeted interventions including a translate-test approach to tackle multilinguality, a visual programming approach to break down complex reasoning, and a method that leverages image captioning to address multimodality. Our interventions achieve the best open performance on this task in a zero-shot setting, boosting open models LLaVA-v1.5-13B by 13.4%, LLaVA-v1.6-34B by 20.3%, and Qwen-VL by 16.7%, while also minorly improving GPT-4V’s performance.</abstract>
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%0 Conference Proceedings
%T What Is Missing in Multilingual Visual Reasoning and How to Fix It
%A Song, Yueqi
%A Khanuja, Simran
%A Neubig, Graham
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F song-etal-2025-missing
%X NLP models today strive for supporting multiple languages and modalities, improving accessibility for diverse users. In this paper, we evaluate their multilingual, multimodal capabilities by testing on a visual reasoning task. We observe that proprietary systems like GPT-4V obtain the best performance on this task now, but open models lag in comparison. Surprisingly, GPT-4V exhibits similar performance between English and other languages, indicating the potential for equitable system development across languages. Our analysis on model failures reveals three key aspects that make this task challenging: multilinguality, complex reasoning, and multimodality. To address these challenges, we propose three targeted interventions including a translate-test approach to tackle multilinguality, a visual programming approach to break down complex reasoning, and a method that leverages image captioning to address multimodality. Our interventions achieve the best open performance on this task in a zero-shot setting, boosting open models LLaVA-v1.5-13B by 13.4%, LLaVA-v1.6-34B by 20.3%, and Qwen-VL by 16.7%, while also minorly improving GPT-4V’s performance.
%R 10.18653/v1/2025.findings-naacl.144
%U https://aclanthology.org/2025.findings-naacl.144/
%U https://doi.org/10.18653/v1/2025.findings-naacl.144
%P 2654-2667

Markdown (Informal)

[What Is Missing in Multilingual Visual Reasoning and How to Fix It](https://aclanthology.org/2025.findings-naacl.144/) (Song et al., Findings 2025)

• What Is Missing in Multilingual Visual Reasoning and How to Fix It (Song et al., Findings 2025)

ACL

• Yueqi Song, Simran Khanuja, and Graham Neubig. 2025. What Is Missing in Multilingual Visual Reasoning and How to Fix It. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 2654–2667, Albuquerque, New Mexico. Association for Computational Linguistics.