@inproceedings{jang-etal-2025-p,
    title = "{P}-{C}o{T}: A Pedagogically-motivated Participatory Chain-of-Thought Prompting for Phonological Reasoning in {LLM}s",
    author = "Jang, Dongjun  and
      Ahn, Youngchae  and
      Shin, Hyopil",
    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/corrections-2025-08/2025.findings-acl.1132/",
    doi = "10.18653/v1/2025.findings-acl.1132",
    pages = "21958--21979",
    ISBN = "979-8-89176-256-5",
    abstract = "This study explores the potential of phonological reasoning within text-based large language models (LLMs). Utilizing the PhonologyBench benchmark, we assess tasks like rhyme word generation, g2p conversion, and syllable counting. Our evaluations across 12 LLMs reveal that while few-shot learning offers inconsistent gains, the introduction of a novel Pedagogically-motivated Participatory Chain-of-Thought (P-CoT) prompt, which is anchored in educational theories like scaffolding and discovery learning, consistently enhances performance. This method leverages structured guidance to activate latent phonological abilities, achieving up to 52{\%} improvement and even surpassing human baselines in certain tasks. Future work could aim to optimize P-CoT prompts for specific models or explore their application across different linguistic domains."
}