BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913858946334720 |
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| author | Wang, Teng Yu, Wing-Yin He, Zhenqi Liu, Zehua Gong, Hailei Wu, Han Han, Xiongwei Shi, Wei She, Ruifeng Zhu, Fangzhou Zhong, Tao |
| author_facet | Wang, Teng Yu, Wing-Yin He, Zhenqi Liu, Zehua Gong, Hailei Wu, Han Han, Xiongwei Shi, Wei She, Ruifeng Zhu, Fangzhou Zhong, Tao |
| contents | LLMs exhibit advanced reasoning capabilities, offering the potential to transform natural language questions into mathematical models. However, existing open-source datasets in operations research domain lack detailed annotations of the modeling process, such as variable definitions, focusing solely on objective values, which hinders reinforcement learning applications. To address this, we release the StructuredOR dataset, annotated with comprehensive labels that capture the complete mathematical modeling process. We further propose BPP-Search, an algorithm that integrates reinforcement learning into a tree-of-thought structure using Beam search, a Process reward model, and a pairwise Preference algorithm. This approach enables efficient exploration of tree structures, avoiding exhaustive search while improving accuracy. Extensive experiments on StructuredOR, NL4OPT, and MAMO-ComplexLP datasets show that BPP-Search significantly outperforms state-of-the-art methods. In tree-based reasoning, BPP-Search excels in accuracy and efficiency, enabling faster retrieval of correct solutions. The StructuredOR dataset is available on Huggingface https://huggingface.co/datasets/LLM4OR/StructuredOR and GitHub https://github.com/LLM4OR/StructuredOR. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_17404 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving Wang, Teng Yu, Wing-Yin He, Zhenqi Liu, Zehua Gong, Hailei Wu, Han Han, Xiongwei Shi, Wei She, Ruifeng Zhu, Fangzhou Zhong, Tao Artificial Intelligence Computation and Language LLMs exhibit advanced reasoning capabilities, offering the potential to transform natural language questions into mathematical models. However, existing open-source datasets in operations research domain lack detailed annotations of the modeling process, such as variable definitions, focusing solely on objective values, which hinders reinforcement learning applications. To address this, we release the StructuredOR dataset, annotated with comprehensive labels that capture the complete mathematical modeling process. We further propose BPP-Search, an algorithm that integrates reinforcement learning into a tree-of-thought structure using Beam search, a Process reward model, and a pairwise Preference algorithm. This approach enables efficient exploration of tree structures, avoiding exhaustive search while improving accuracy. Extensive experiments on StructuredOR, NL4OPT, and MAMO-ComplexLP datasets show that BPP-Search significantly outperforms state-of-the-art methods. In tree-based reasoning, BPP-Search excels in accuracy and efficiency, enabling faster retrieval of correct solutions. The StructuredOR dataset is available on Huggingface https://huggingface.co/datasets/LLM4OR/StructuredOR and GitHub https://github.com/LLM4OR/StructuredOR. |
| title | BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2411.17404 |