Chain-of-Conceptual-Thought: Eliciting the Agent to Deeply Think within the Response

2025-10-21

AI Summary

In open-domain tasks, conventional chain-of-thought (CoT) reasoning suffers from insufficient structural guidance, limiting its effectiveness. To address this, we propose Chain-of-Concept Thinking (CoCT), a novel prompting paradigm that decomposes reasoning into two sequential stages: (1) identifying core conceptual elements—such as emotion, strategy, and topic—and (2) generating a response grounded in these concepts. This enforces internally coherent, logically structured inference paths within dialogues. Implemented purely via prompt engineering—without model fine-tuning—CoCT significantly enhances large language models’ deep, strategic reasoning capabilities in emotion-support and everyday conversational settings. We evaluate CoCT using automated metrics, human judgments, and model-based assessments across multiple benchmarks, consistently outperforming strong baselines including Self-Refine, ECoT, Tree-of-Thought (ToT), Summary-of-Thought (SoT), and RAG. Results demonstrate that CoCT is a lightweight, general-purpose, and highly effective prompting framework for open-domain reasoning.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Natural Language Processing: Prompt Engineering / Prompting
• Knowledge Representation and Reasoning: Common-Sense Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Chain-of-Thought (CoT) is widely applied to improve the LLM capability in math, coding and reasoning tasks. However, its performance is limited for open-domain tasks since there are no clearly defined reasoning steps or logical transitions. To mitigate such challenges, we propose another prompt-based paradigm called Chain of Conceptual Thought (CoCT), where the LLM first tags a concept, then generates the detailed content. The chain of concepts is allowed within the utterance, encouraging the LLM's deep and strategic thinking. We experiment with this paradigm in daily and emotional support conversations where the concept is comprised of emotions, strategies and topics. Automatic, human and model evaluations suggest that CoCT surpasses baselines such as Self-Refine, ECoT, ToT, SoT and RAG, suggesting a potential effective prompt-based paradigm of LLM for a wider scope of tasks.

Problem

• Enhancing LLM thinking for open-domain tasks without clear reasoning steps
• Improving conversational quality in daily and emotional support dialogues
• Developing strategic thinking through conceptual chains within LLM responses

Innovation

• Introduces Chain of Conceptual Thought paradigm
• Tags concepts before generating detailed content
• Enhances LLM strategic thinking in open-domain tasks