Unlocking Structured Thinking in Language Models with Cognitive Prompting

• URL: http://arxiv.org/abs/2410.02953v3
• Date: Sat, 30 Nov 2024 12:16:51 GMT
• Title: Unlocking Structured Thinking in Language Models with Cognitive Prompting
• Authors: Oliver Kramer, Jill Baumann,
• Abstract summary: We propose cognitive prompting as a novel approach to guide problem-solving in large language models (LLMs) We introduce three variants: a deterministic sequence of cognitive operations, a self-adaptive variant, and a hybrid variant. Experiments with LLaMA, Gemma2, and Qwen models in each two sizes on the arithmetic reasoning benchmark GSM8K demonstrate that cognitive prompting significantly improves performance compared to standard question answering.
• Score: 0.0
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• Abstract: We propose cognitive prompting as a novel approach to guide problem-solving in large language models (LLMs) through structured, human-like cognitive operations, such as goal clarification, decomposition, filtering, abstraction, and pattern recognition. By employing systematic, step-by-step reasoning, cognitive prompting enables LLMs to tackle complex, multi-step tasks more efficiently. We introduce three variants: a deterministic sequence of cognitive operations, a self-adaptive variant in which the LLM dynamically selects the sequence of cognitive operations, and a hybrid variant that uses generated correct solutions as few-shot chain-of-thought prompts. Experiments with LLaMA, Gemma~2, and Qwen models in each two sizes on the arithmetic reasoning benchmark GSM8K demonstrate that cognitive prompting significantly improves performance compared to standard question answering.

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