Summary of Expanding Search Space with Diverse Prompting Agents: An Efficient Sampling Approach For Llm Mathematical Reasoning, by Gisang Lee et al.
Expanding Search Space with Diverse Prompting Agents: An Efficient Sampling Approach for LLM Mathematical Reasoning
by Gisang Lee, Sangwoo Park, Junyoung Park, Andrew Chung, Sieun Park, Yoonah Park, Byungju Kim, Min-gyu Cho
First submitted to arxiv on: 13 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 Medium Difficulty summary: This paper investigates the capabilities of Large Language Models (LLMs) in mathematical reasoning tasks by analyzing various prompting methods. Traditional approaches often rely on self-consistency within single prompting methods, restricting the exploration of diverse problem-solving strategies. The study’s findings show that each method explores a distinct search space, which becomes more evident with increasing problem complexity. To leverage this phenomenon, an efficient sampling process is applied to uniformly combine samples from these diverse methods. This approach expands the maximum search space and achieves higher performance with fewer runs compared to single methods. Notably, when applied to the MATH-hard subset of the MATH dataset, approximately 43% fewer runs were required on average while achieving the maximum search space. The study highlights the importance of integrating diverse problem-solving strategies to enhance the reasoning abilities of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research looks at how computers can solve math problems better by trying different ways to ask questions. Usually, computers are only taught one way to ask questions and get stuck if they’re not good at it. The scientists found that if they let the computer try many different ways of asking questions, it gets really good at solving hard math problems! They also discovered that using all these different methods helps the computer find better answers faster than just using one method. This is important because it could help computers solve even harder math problems in the future. |
Keywords
» Artificial intelligence » Prompting
