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.

大型语言模型的自动启动图形范例 (Auto-Prompt Graphical Paradigm,APGP) 可以通过结合刺激和框架启动来提高 LLM 在多个领域中的问题解决能力，进而实现问题抽象、多样化解决方案生成、全面优化以及提供答案后的自我验证，从而提高 LLM 在问题解决方面的效率和准确性，并为 LLM 的新应用开辟了新的道路。

情绪刺激与提示设计的交会：一种自动提示图形范式

When Emotional Stimuli meet Prompt Designing: An Auto-Prompt Graphical  Paradigm

Large language models (LLMs) demonstrate exceptional performance in numerous
tasks but still heavily rely on knowledge stored in their parameters. Moreover,
updating this knowledge incurs high training costs. Retrieval-augmented
generation (RAG) methods address this issue by integrating external knowledge.
The model can answer questions it couldn't previously by retrieving knowledge
relevant to the query. This approach improves performance in certain scenarios
for specific tasks. However, if irrelevant texts are retrieved, it may impair
model performance. In this paper, we propose Retrieval Augmented Iterative
Self-Feedback (RA-ISF), a framework that iteratively decomposes tasks and
processes them in three submodules to enhance the model's problem-solving
capabilities. Experiments show that our method outperforms existing benchmarks,
performing well on models like GPT3.5, Llama2, significantly enhancing factual
reasoning capabilities and reducing hallucinations.

我们提出了一种迭代检索增强自我反馈 (RA-ISF) 框架，该框架将任务迭代分解并在三个子模块中处理，以增强模型的解决问题的能力。实验证明，我们的方法优于现有的基准，在像 GPT3.5、Llama2 这样的模型上表现良好，显著增强事实推理能力并减少虚幻现象。