Summary of Mathdivide: Improved Mathematical Reasoning by Large Language Models, By Saksham Sahai Srivastava et al.
MathDivide: Improved mathematical reasoning by large language models
by Saksham Sahai Srivastava, Ashutosh Gandhi
First submitted to arxiv on: 12 May 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 This paper proposes a novel prompting technique for large language models (LLMs) to perform mathematical reasoning tasks, called MathDivide. The approach involves breaking down complex math problems into simpler subproblems and using Python code generated by the LLM to evaluate the expressions. By composing the solutions of these subproblems, the final answer is obtained and compared to the correct answer. If the answers match, it is produced as output; otherwise, a refinement prompt is fed to the LLM. The authors experiment with MathDivide on both closed-source and open-source LLM models using the GSM8K dataset, achieving significant outperformance compared to leading techniques like Math-prompter. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses large language models to do math problems better. It’s like a special trick to help them understand what we want them to do. The trick is called MathDivide and it breaks down big math problems into smaller ones that are easier for the model to solve. Then, it puts the answers together to get the final answer. This approach works really well on lots of different math problems! |
Keywords
* Artificial intelligence * Prompt * Prompting
