Summary of Jmmmu: a Japanese Massive Multi-discipline Multimodal Understanding Benchmark For Culture-aware Evaluation, by Shota Onohara et al.
JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation
by Shota Onohara, Atsuyuki Miyai, Yuki Imajuku, Kazuki Egashira, Jeonghun Baek, Xiang Yue, Graham Neubig, Kiyoharu Aizawa
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces JMMMU, the first large-scale Japanese benchmark designed to evaluate Large Multimodal Models (LMMs) on expert-level tasks specific to the Japanese cultural context. The authors aim to accelerate research on LMMs in non-English languages by providing a comprehensive culture-aware evaluation framework. The JMMMU benchmark features two subsets: culture-agnostic (CA), which allows one-to-one comparison with its English counterpart MMMU, and culture-specific (CS), comprising newly crafted subjects that reflect Japanese cultural context. Experimental results show that many LMMs perform poorly when evaluated in Japanese due to language variation, while others demonstrate inadequate cultural understanding. The authors hope that this work will advance LMM performance in Japanese and serve as a guideline for creating high-standard, culturally diverse benchmarks for multilingual LMM development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LMMs are special types of artificial intelligence models that can understand and process different forms of data like images, text, and audio. This paper is about creating a new benchmark to test these models in Japanese. Right now, there isn’t a good way to evaluate how well LMMs work in languages other than English. The authors created two parts to their benchmark: one that’s language-agnostic (meaning it doesn’t rely on any specific language), and another that’s culture-specific (meaning it reflects the unique cultural context of Japan). They found that many LMMs don’t perform well when tested in Japanese because they aren’t adapted for that language, while others show a lack of understanding of Japanese culture. The authors hope their work will help improve how well LMMs work in Japanese and create better standards for evaluating these models across different languages. |
