Summary of Macm: Utilizing a Multi-agent System For Condition Mining in Solving Complex Mathematical Problems, by Bin Lei et al.
MACM: Utilizing a Multi-Agent System for Condition Mining in Solving Complex Mathematical Problems
by Bin Lei, Yi Zhang, Shan Zuo, Ali Payani, Caiwen Ding
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Multiagent Systems (cs.MA)
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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 Recent advancements in large language models, particularly GPT-4, have shown impressive capabilities in processing standard queries. However, their performance significantly drops when dealing with advanced mathematical problems requiring complex, multi-step logical reasoning. To enhance their inferential capabilities, researchers have explored prompting engineering methodologies, such as the Tree of Thought and Graph of Thought. Despite these efforts, existing approaches face two significant limitations: constrained effectiveness in tackling complex mathematical problems and a need for distinct prompts for individual problems, hindering generalizability. This paper introduces the Multi-Agent System for conditional Mining (MACM) prompting method, which not only resolves intricate mathematical problems but also demonstrates strong generalization capabilities across various mathematical contexts. With MACM assistance, GPT-4 Turbo’s accuracy on level five mathematical problems in the MATH dataset increases from 54.68% to 76.73%. The code is available at https://github.com/bin123apple/MACM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how computers can do math better. Right now, computer models like GPT-4 are really good at answering simple questions, but they struggle with harder math problems that need a lot of thinking. To help them get better, researchers have been trying different ways to give them prompts. But these methods have some problems: they don’t work well for hard math problems and require different prompts for each problem. This paper introduces a new way to give prompts called MACM. It helps computers solve complex math problems and can even do it in different math contexts. With this method, the computer model GPT-4 Turbo got 76.73% correct on very challenging math problems. You can look at the code for this method on the website https://github.com/bin123apple/MACM. |
Keywords
» Artificial intelligence » Generalization » Gpt » Prompting
