Summary of Multimath: Bridging Visual and Mathematical Reasoning For Large Language Models, by Shuai Peng et al.
MultiMath: Bridging Visual and Mathematical Reasoning for Large Language Models
by Shuai Peng, Di Fu, Liangcai Gao, Xiuqin Zhong, Hongguang Fu, Zhi Tang
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper introduces MultiMath-7B, a multimodal large language model that bridges the gap between mathematical reasoning and visual inputs. The model is trained through a four-stage process, focusing on vision-language alignment, visual and math instruction-tuning, and process-supervised reinforcement learning. To evaluate the model’s performance, a novel dataset called MultiMath-300K was constructed, spanning K-12 levels with image captions and step-wise solutions. The model achieves state-of-the-art performance among open-source models on existing multimodal mathematical benchmarks and also excels on text-only mathematical benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MultiMath-7B is a new kind of artificial intelligence that can understand math problems and pictures at the same time. This helps with solving math problems because many math tasks involve looking at diagrams, charts, or function plots. The researchers created this AI by training it to align words and images, and then tested it on many math problems. They also made a new dataset of 300,000 math problems that includes pictures and step-by-step solutions. This AI is really good at solving math problems and can even do better than other AI models. |
Keywords
» Artificial intelligence » Alignment » Instruction tuning » Large language model » Reinforcement learning » Supervised
