What Is Missing in Multilingual Visual Reasoning and How to Fix It (2025.findings-naacl)
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| Challenge: | NLP models today strive for supporting multiple languages and modalities, improving accessibility for diverse users. |
| Approach: | They propose a translation-test approach to tackle multilinguality, visual programming approach to break down complex reasoning, and a method that leverages image captioning to address multimodality. |
| Outcome: | The proposed interventions boost open models LLaVA-v1.5-13B by 13.4%, LLva-v1.6-34B by 20.3%, and Qwen-VL by 16.7% while minorly improving GPT-4V’s performance. |
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| Challenge: | Large language models (LLMs) have demonstrated multilingual capabilities, yet they are mostly English-centric due to the imbalanced training corpora. |
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| Challenge: | Recent reasoning language models (RLMs) achieve strong performance on complex reasoning tasks, yet they still exhibit a multilingual reasoning gap. |
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