Summary of Assessing the Creativity Of Llms in Proposing Novel Solutions to Mathematical Problems, by Junyi Ye et al.
Assessing the Creativity of LLMs in Proposing Novel Solutions to Mathematical Problems
by Junyi Ye, Jingyi Gu, Xinyun Zhao, Wenpeng Yin, Guiling Wang
First submitted to arxiv on: 24 Oct 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 explores the creative potential of Large Language Models (LLMs) in mathematical reasoning. It argues that AI systems should not only produce correct answers but also assist humans in developing novel solutions to mathematical challenges. The authors introduce a new framework and benchmark, CreativeMath, which assesses LLMs’ ability to propose innovative solutions after some known solutions have been provided. Experiments show that while LLMs perform well on standard mathematical tasks, their capacity for creative problem-solving varies considerably. Notably, the Gemini-1.5-Pro model outperformed other LLMs in generating novel solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI systems can do more than just get the right answers to math problems. They can also help humans come up with new solutions! This paper looks at how well big language models do this kind of creative problem-solving. The researchers created a special test, called CreativeMath, that shows LLMs trying to solve math problems in new and interesting ways. They found that while the models are good at regular math problems, they’re not all equally good at coming up with new ideas. One model, called Gemini-1.5-Pro, did really well at this kind of creative thinking. |
Keywords
» Artificial intelligence » Gemini
