Technology Category
Machine Learning: Large Multimodal Models (LMMs)Cognitive Modeling & Cognitive Systems: Conceptual Inference and ReasoningNatural Language Processing: (Large) Language Models
Chain of Methodologies: Scaling Test Time Computation without Training
🤖 AI Summary
Large language models (LLMs) exhibit limited performance on complex reasoning tasks, primarily due to the absence of structured methodological knowledge—such as divide-and-conquer, abductive reasoning, and analogical reasoning—in their training data. Method: We propose Chain-of-Methodology (CoM), a training-free prompting framework that explicitly encodes general human methodologies as reusable templates embeddable within reasoning chains, augmented by a metacognitive guidance mechanism to elicit systematic thinking and self-unfolding inference. CoM requires no fine-tuning or external tools, relying solely on prompt engineering. Contribution/Results: Evaluated across mathematical reasoning, multi-hop question answering, and scientific reasoning benchmarks, CoM consistently outperforms state-of-the-art prompting methods—including Chain-of-Thought (CoT) and Auto-CoT—demonstrating that injecting structured methodology effectively bridges the gap between LLM inference and human-like reasoning paradigms. This work establishes a novel, training-agnostic paradigm for high-order reasoning.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Enhancing LLMs' structured thinking for complex reasoning tasks
Activating systematic reasoning without explicit fine-tuning
Bridging the gap toward human-level reasoning with methodologies
Innovation
Methods, ideas, or system contributions that make the work stand out.
Chain of Methodologies enhances structured thinking
Leverages metacognitive abilities without fine-tuning
Training-free prompting for complex reasoning tasks
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