When Emotional Stimuli meet Prompt Designing: An Auto-Prompt Graphical Paradigm
This work addresses the problem of manually designing prompts for LLMs, offering an automated approach that is incremental in integrating existing prompt types.
The paper tackled the challenge of enhancing large language models' problem-solving by proposing an Auto-Prompt Graphical Paradigm that combines stimulating and framework prompts, with test results on ruozhiba and BBH datasets showing improved efficiency and accuracy.
With the development of Large Language Models (LLM), numerous prompts have been proposed, each with a rich set of features and their own merits. This paper summarizes the prompt words for large language models (LLMs), categorizing them into stimulating and framework types, and proposes an Auto-Prompt Graphical Paradigm(APGP) that combines both stimulating and framework prompts to enhance the problem-solving capabilities of LLMs across multiple domains, then exemplifies it with a framework that adheres to this paradigm. The framework involves automated prompt generation and consideration of emotion-stimulus factors, guiding LLMs in problem abstraction, diversified solutions generation, comprehensive optimization, and self-verification after providing answers, ensuring solution accuracy. Compared to traditional stimuli and framework prompts, this framework integrates the advantages of both by adopting automated approaches inspired by APE work, overcoming the limitations of manually designed prompts. Test results on the ruozhiba and BBH datasets demonstrate that this framework can effectively improve the efficiency and accuracy of LLMs in problem-solving, paving the way for new applications of LLMs.