@inproceedings{wang-etal-2025-lets,
    title = "Let{'}s Reason Formally: Natural-Formal Hybrid Reasoning Enhances {LLM}{'}s Math Capability",
    author = "Wang, Ruida  and
      Li, Yuxin  and
      Fung, Yi R.  and
      Zhang, Tong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.850/",
    pages = "16794--16820",
    ISBN = "979-8-89176-332-6",
    abstract = "Enhancing the mathematical reasoning capabilities of LLMs has garnered significant attention in both the mathematical and computer science communities. Recent works have made substantial progress in both Natural Language (NL) reasoning and Formal Language (FL) reasoning by leveraging the potential of pure Reinforcement Learning (RL) methods on base models. However, RL approaches struggle to impart new capabilities not presented in the base model, highlighting the need to integrate more knowledge like FL into NL math reasoning effectively. Yet, this integration is challenging due to inherent disparities in problem structure and reasoning format between NL and FL. To address these challenges, we introduce **NL-FL HybridReasoning (NFL-HR)**, an end-to-end framework designed to incorporate the FL expert into NL math problem-solving. To bridge the NL and FL input format gap, we propose the *NL-FL Problem Alignment* method, which reformulates the Question-Answering (QA) problems in NL as existence theorems in FL. Subsequently, the *Mixed Problem Input* technique we provide enables the FL reasoner to handle both QA and existence problems concurrently. Lastly, we mitigate the NL and FL output format gap in reasoning through an LLM-based *Answer Extraction* mechanism. Comprehensive experiments demonstrate that the **NFL-HR** framework achieves **89.80{\%}** and **84.34{\%}** accuracy rates on the MATH-500 and the AMC benchmarks, surpassing the NL baseline by 4.60{\%} and 4.82{\%}, respectively. Notably, some problems resolved by our framework remain unsolved by the NL baseline model even under a larger number of trials."
}