用智力结构模型提升大模型的逻辑推理能力
Cognitive Prompts Using Guilford's Structure of Intellect Model
- 基于吉尔福德智力结构模型设计思维引导提示
- 增强大模型回答的条理性和适应性
- 适合需要严谨逻辑推理的应用场景
大型语言模型（LLMs）虽具备强大的语言生成能力，但在结构化推理方面常表现不佳，导致问题解决过程不一致或效果欠佳。为缓解这一局限，本文借鉴智力理论中的吉尔福德智力结构（SOI）模型——该模型将认知操作分为模式识别、记忆检索和评估等类别——作为认知提示工程的基础框架。本文提出一种新型认知提示方法，通过引入SOI启发的推理机制，系统性地提升大模型在推理与决策中的清晰度、连贯性与适应性，旨在改善其响应质量。
原文摘要 · Abstract (English)
Large language models (LLMs) demonstrate strong language generation capabilities but often struggle with structured reasoning, leading to inconsistent or suboptimal problem-solving. To mitigate this limitation, Guilford's Structure of Intellect (SOI) model - a foundational framework from intelligence theory - is leveraged as the basis for cognitive prompt engineering. The SOI model categorizes cognitive operations such as pattern recognition, memory retrieval, and evaluation, offering a systematic approach to enhancing LLM reasoning and decision-making. This position paper presents a novel cognitive prompting approach for enforcing SOI-inspired reasoning for improving clarity, coherence, and adaptability in model responses.
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