@inproceedings{liu-etal-2025-chain-methodologies,
    title = "Chain of Methodologies: Scaling Test Time Computation without Training",
    author = "Liu, Cong  and
      Wu, Jie  and
      Wu, Weigang  and
      Chen, Xu  and
      Lin, Liang  and
      Zheng, Wei-Shi",
    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.276/",
    doi = "10.18653/v1/2025.findings-acl.276",
    pages = "5298--5312",
    ISBN = "979-8-89176-256-5",
    abstract = "Large Language Models (LLMs) often struggle with complex reasoning tasks due to insufficient in-depth insights in their training data, which are frequently absent in publicly available documents. This paper introduces the Chain of Methodologies (CoM), a simple and innovative iterative prompting framework designed to build structured reasoning processes by injecting human methodological insights, thereby enabling LLMs to perform long and effective reasoning for complex tasks. Assuming that LLMs possess certain metacognitive abilities, CoM leverages user-defined methodologies to stimulate the cognitive insights that LLMs have learned implicitly from training data. Experimental results indicate that CoM outperforms competitive baselines, highlighting the potential of training-free prompting methods as general solutions for complex reasoning tasks and the possibility of incorporating human-like methodological insights to bridge the gap to human-level reasoning."
}