Chain of Methodologies: Scaling Test Time Computation without Training
This addresses the challenge of insufficient reasoning capabilities in LLMs for complex tasks, offering a training-free solution, though it appears incremental as it builds on existing prompting methods.
The paper tackles the problem of large language models struggling with complex reasoning tasks by introducing Chain of Methodologies (CoM), a prompting framework that integrates human methodological insights to enhance structured thinking, and experiments show it surpasses competitive baselines.
Large Language Models (LLMs) often struggle with complex reasoning tasks due to insufficient in-depth insights in their training data, which are typically absent in publicly available documents. This paper introduces the Chain of Methodologies (CoM), an innovative and intuitive prompting framework that enhances structured thinking by integrating human methodological insights, enabling LLMs to tackle complex tasks with extended reasoning. CoM leverages the metacognitive abilities of advanced LLMs, activating systematic reasoning throught user-defined methodologies without explicit fine-tuning. Experiments show that CoM surpasses competitive baselines, demonstrating the potential of training-free prompting methods as robust solutions for complex reasoning tasks and bridging the gap toward human-level reasoning through human-like methodological insights.