Summary of Mllm-sr: Conversational Symbolic Regression Base Multi-modal Large Language Models, by Yanjie Li et al.
MLLM-SR: Conversational Symbolic Regression base Multi-Modal Large Language Models
by Yanjie Li, Weijun Li, Lina Yu, Min Wu, Jingyi Liu, Wenqiang Li, Shu Wei, Yusong Deng
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 This research paper presents a novel approach to symbolic regression, a problem in artificial intelligence where algorithms generate mathematical formulas from observed data. The existing methods directly produce formulas without considering specific requirements, which may require complex operations and be inconvenient. In contrast, the proposed MLLM-SR method uses multi-modal large language models to generate formulas that meet specific requirements by describing them with natural language instructions. The paper demonstrates the effectiveness of MLLM-SR on the Nguyen dataset, achieving state-of-the-art performance in fitting accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study develops a new way for artificial intelligence to find mathematical formulas from data. The usual method just gives you an answer without considering what you want it to be like. But this new approach lets you tell the computer exactly what kind of formula you need, using simple language instructions. This is really important because it can help us better understand complex relationships between things. In testing, this new method worked much better than others at finding the right formulas and also understood when we added special information to guide it. |
Keywords
» Artificial intelligence » Multi modal » Regression
