Summary of Bpp-search: Enhancing Tree Of Thought Reasoning For Mathematical Modeling Problem Solving, by Teng Wang et al.
BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving
by Teng Wang, Wing-Yin Yu, Zhenqi He, Zehua Liu, Xiongwei Han, Hailei Gong, Han Wu, Wei Shi, Ruifeng She, Fangzhou Zhu, Tao Zhong
First submitted to arxiv on: 26 Nov 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 LLMs have demonstrated impressive reasoning capabilities, which can be leveraged to transform natural language questions into mathematical models. However, existing datasets in the operations research domain lack detailed annotations of the modeling process, hindering applications in reinforcement learning. To address this, we introduce the StructuredOR dataset, annotated with comprehensive labels that capture the complete mathematical modeling process. We also propose BPP-Search, an algorithm that integrates reinforcement learning into a tree-of-thought structure using Beam search, Process reward model, and Pairwise Preference algorithm. This approach enables efficient exploration of tree structures, improving accuracy while avoiding exhaustive search. Our experiments on StructuredOR, NL4OPT, and MAMO-ComplexLP datasets show that BPP-Search significantly outperforms state-of-the-art methods in tree-based reasoning, excelling in both accuracy and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a super smart computer program that can understand natural language questions and turn them into math problems. This is called “language-to-math” and it’s really cool! The problem is that the data used to train these programs lacks important information about how to solve the math problems, making it hard to use for certain tasks. To fix this, we created a new dataset with extra details on how to solve math problems. We also came up with a way to use reinforcement learning to find the best solutions faster and more accurately. Our tests show that our method is better than what’s currently available. |
Keywords
» Artificial intelligence » Reinforcement learning
