Cognitive Prompts Using Guilford's Structure of Intellect Model
This addresses inconsistent problem-solving in LLMs for AI researchers, but it is incremental as it applies an existing framework to a new context.
The paper tackles the problem of large language models struggling with structured reasoning by leveraging Guilford's Structure of Intellect model for cognitive prompt engineering, resulting in improved clarity, coherence, and adaptability in model responses.
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.