MathVerse, a new benchmark for evaluating multimodal large language models (MLLMs) in visual mathematical problem solving, was reported by Webmaster Home. This benchmark tested the performance of multiple MLLMs in processing mathematical problems containing visual information. The results showed that most models relied heavily on visual input, while GPT-4V performed well on both text and visual input. This research provides a valuable reference for the development of future MLLMs, and also prompts developers to further pay attention to the model's ability to process different modal information.
The article focuses on the results of the MathVerse benchmark, highlighting the excellent performance of GPT-4V and the dependence of most models on visual input. This research is of great significance in promoting the development of multi-modal large-scale language models. In the future, more and more powerful models will emerge to better handle complex tasks containing visual information.