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Cognitive Prompts Using Guilford's Structure of Intellect Model

arXiv 2503.22036 ↗First posted 27 Mar 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 9 references that are in the map. First posted on arXiv 27 Mar 2025.

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6 of 9 references.

References · 6

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,045
  4. 2023Large Language Models as Optimizerscited by 864
  5. 2023Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolutioncited by 551
  6. 2024Unlocking Structured Thinking in Language Models with Cognitive Promptingcited by 8

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  1. 2025Mapping the Minds of LLMs: A Graph-Based Analysis of Reasoning LLM
  2. 2025Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers
  3. 2025Cognitive Foundations for Reasoning and Their Manifestation in LLMs
  4. 2025Why Prompt Design Matters and Works: A Complexity Analysis of Prompt Search Space in LLMs
  5. 2024Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles
  6. 2026Beyond the Answer: Decoding the Behavior of LLMs as Scientific Reasoners
  7. 2023Metacognitive Prompting Improves Understanding in Large Language Models
  8. 2025Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize?