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    <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.</abstract>
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%0 Conference Proceedings
%T Chain of Methodologies: Scaling Test Time Computation without Training
%A Liu, Cong
%A Wu, Jie
%A Wu, Weigang
%A Chen, Xu
%A Lin, Liang
%A Zheng, Wei-Shi
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F liu-etal-2025-chain-methodologies
%X 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.
%R 10.18653/v1/2025.findings-acl.276
%U https://aclanthology.org/2025.findings-acl.276/
%U https://doi.org/10.18653/v1/2025.findings-acl.276
%P 5298-5312

Markdown (Informal)

[Chain of Methodologies: Scaling Test Time Computation without Training](https://aclanthology.org/2025.findings-acl.276/) (Liu et al., Findings 2025)

• Chain of Methodologies: Scaling Test Time Computation without Training (Liu et al., Findings 2025)

ACL

• Cong Liu, Jie Wu, Weigang Wu, Xu Chen, Liang Lin, and Wei-Shi Zheng. 2025. Chain of Methodologies: Scaling Test Time Computation without Training. In Findings of the Association for Computational Linguistics: ACL 2025, pages 5298–5312, Vienna, Austria. Association for Computational Linguistics.