JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation
Shota Onohara, Atsuyuki Miyai, Yuki Imajuku, Kazuki Egashira, Jeonghun Baek, Xiang Yue, Graham Neubig, Kiyoharu Aizawa
- Anthology ID:
- 2025.naacl-long.43
- Volume:
- Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
- Month:
- April
- Year:
- 2025
- Address:
- Albuquerque, New Mexico
- Editors:
- Luis Chiruzzo, Alan Ritter, Lu Wang
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 932–950
- Language:
- URL:
- https://preview.aclanthology.org/moar-dois/2025.naacl-long.43/
- DOI:
- 10.18653/v1/2025.naacl-long.43
- Cite (ACL):
- Shota Onohara, Atsuyuki Miyai, Yuki Imajuku, Kazuki Egashira, Jeonghun Baek, Xiang Yue, Graham Neubig, and Kiyoharu Aizawa. 2025. JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 932–950, Albuquerque, New Mexico. Association for Computational Linguistics.
- Cite (Informal):
- JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation (Onohara et al., NAACL 2025)
- PDF:
- https://preview.aclanthology.org/moar-dois/2025.naacl-long.43.pdf