Summary of When Emotional Stimuli Meet Prompt Designing: An Auto-prompt Graphical Paradigm, by Chenggian Ma et al.
When Emotional Stimuli meet Prompt Designing: An Auto-Prompt Graphical Paradigm
by Chenggian Ma, Xiangyu Zhao, Chunhui Zhang, Yanzhao Qin, Wentao Zhang
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to enhancing the problem-solving capabilities of Large Language Models (LLMs) across multiple domains. The authors categorize prompts into stimulating and framework types and propose an Auto-Prompt Graphical Paradigm (APGP) that combines both types to improve LLMs’ abilities in abstraction, solution generation, optimization, and verification. The APGP framework uses automated prompt generation and considers emotion-stimulus factors to guide LLMs in problem-solving. Compared to traditional stimuli and framework prompts, the APGP integrates the advantages of both by adopting automated approaches inspired by APE work, overcoming limitations of manually designed prompts. Experimental results on the ruozhiba and BBH datasets demonstrate that this framework can effectively improve the efficiency and accuracy of LLMs in problem-solving. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving how computers learn to solve problems. The authors found a way to make computer language models better at solving problems by using different types of prompts. They combined two kinds of prompts, “stimulating” and “framework”, to create a new approach called APGP. This framework helps the language models understand problems better, generate more solutions, and check their answers for accuracy. The authors tested this approach on some datasets and showed that it can make language models work more efficiently and accurately. This could lead to new ways of using language models in the future. |
Keywords
* Artificial intelligence * Optimization * Prompt
