M4U: Evaluating Multilingual Understanding and Reasoning for Large Multimodal Models

TL;DR

This paper introduces M4U, a comprehensive benchmark for evaluating multilingual multimodal understanding and reasoning in large models, revealing current models' limitations in cross-lingual multimodal reasoning across diverse scientific disciplines.

Contribution

The paper presents M4U, a new challenging benchmark with 10,000 samples across 64 disciplines in six languages, and provides extensive evaluations of state-of-the-art multilingual multimodal models.

Findings

GPT-4o achieves only 47.6% accuracy on M4U.

Leading models show significant language preferences.

Models struggle with cross-lingual multimodal reasoning.

Abstract

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.