Recently, Huazhong University of Science and Technology and other institutions jointly released a new benchmark for multi-modal large models. The benchmark covers five major tasks and 27 data sets, providing a more comprehensive standard for the evaluation of multi-modal large models. The release of this benchmark aims to promote the development of multi-modal large model technology and promote its application in various fields. Evaluation results on the new benchmark show that existing models perform well on some tasks but still have shortcomings on others, which provides an important reference for future research directions.
Huazhong University of Science and Technology and other institutions released a new benchmark for multi-modal large models, covering five major tasks and 27 data sets. Evaluation results show excellent performance on tasks such as text recognition and document question answering, but there are challenges in semantic dependencies, handwritten texts and multilingual texts. The research team built OCRBench to more accurately evaluate OCR capabilities and provide guidance for the development of multi-modal large models. The introduction of OCRBench provides researchers with comprehensive tools to promote the accurate evaluation and improvement of multi-modal large models in the field of OCR.
This research result not only provides valuable experience for the development of multi-modal large models, but also lays a solid foundation for promoting the application of artificial intelligence technology in a wider range of fields. In the future, we expect more similar research to help us better understand and apply multi-modal large models, thereby achieving breakthrough progress in artificial intelligence technology.