M4U: Evaluating Multilingual Understanding and Reasoning for Large Multimodal Models
This addresses the problem of inadequate evaluation for multilingual multimodal models, which is crucial for deployment across diverse languages, though it is incremental as it builds on existing benchmarking efforts.
The authors tackled the lack of a challenging benchmark for evaluating multilingual multimodal reasoning by introducing M4U, a dataset covering 64 disciplines in six languages, and found that state-of-the-art models like GPT-4o achieve only 47.6% average accuracy, revealing significant language preferences and struggles with cross-lingual reasoning.
Multilingual capability is an essential aspect for large multimodal models, since they are usually deployed across various countries and languages. However, most existing benchmarks for multilingual multimodal reasoning struggle to differentiate between models of varying performance; even language models without visual capabilities can easily achieve high scores. This leaves a comprehensive evaluation of leading multilingual multimodal models largely unexplored. In this work, we introduce M4U, a novel and challenging benchmark for assessing the capability of multi-discipline multilingual multimodal understanding and reasoning. M4U contains 10k samples covering 64 disciplines across 16 subfields in Science, Engineering, and Healthcare in six languages. Using M4U, we conduct extensive evaluations of leading Large Multimodal Models (LMMs) and Large Language Models (LLMs) with external tools. The evaluation results demonstrate that the state-of-the-art model, GPT-4o, achieves only 47.6% average accuracy on M4U. Additionally, we observe that the leading LMMs exhibit significant language preferences. Our in-depth analysis indicates that leading LMMs, including GPT-4o, struggle to perform reasoning using multilingual information present in both visual and textual context. Specifically, they suffer performance degradation when prompted with cross-lingual multimodal questions. Our code and dataset is public available.