@inproceedings{payoungkhamdee-etal-2025-towards,
    title = "Towards Better Understanding of Program-of-Thought Reasoning in Cross-Lingual and Multilingual Environments",
    author = "Payoungkhamdee, Patomporn  and
      Tuchinda, Pume  and
      Baek, Jinheon  and
      Cahyawijaya, Samuel  and
      Udomcharoenchaikit, Can  and
      Manakul, Potsawee  and
      Limkonchotiwat, Peerat  and
      Chuangsuwanich, Ekapol  and
      Nutanong, Sarana",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.findings-acl.817/",
    pages = "15810--15828",
    ISBN = "979-8-89176-256-5",
    abstract = "Multi-step reasoning is essential for large language models (LLMs), yet multilingual performance remains challenging. While Chain-of-Thought (CoT) prompting improves reasoning, it struggles with non-English languages due to the entanglement of reasoning and execution. Program-of-Thought (PoT) prompting separates reasoning from execution, offering a promising alternative but shifting the challenge to generating programs from non-English questions. We propose a framework to evaluate PoT by separating multilingual reasoning from code execution to examine (i) the impact of fine-tuning on question-reasoning alignment and (ii) how reasoning quality affects answer correctness. Our findings demonstrate that PoT fine-tuning substantially enhances multilingual reasoning, outperforming CoT fine-tuned models. We further demonstrate a strong correlation between reasoning quality (measured through code quality) and answer accuracy, highlighting its potential as a test-time performance improvement heuristic."
}