Autonomous Prompt Engineering in Large Language Models
<|reference_start|>Autonomous Prompt Engineering in Large Language Models: Prompt engineering is a crucial yet challenging task for optimizing the performance of large language models (LLMs) on customized tasks. This pioneering research introduces the Automatic Prompt Engineering Toolbox (APET), which enables GPT-4 to autonomously apply prompt engineering techniques. By leveraging sophisticated strategies such as Expert Prompting, Chain of Thought, and Tree of Thoughts, APET empowers GPT-4 to dynamically optimize prompts, resulting in substantial improvements in tasks like Word Sorting (4.4% increase) and Geometric Shapes (6.8% increase). Despite encountering challenges in complex tasks such as Checkmate in One (-14.8%), these findings demonstrate the transformative potential of APET in automating complex prompt optimization processes without the use of external data. Overall, this research represents a significant leap in AI development, presenting a robust framework for future innovations in autonomous AI systems and highlighting the ability of GPT-4 to bring prompt engineering theory to practice. It establishes a foundation for enhancing performance in complex task performance and broadening the practical applications of these techniques in real-world scenarios.<|reference_end|>
@article{kepel2024autonomous,
    title={Autonomous Prompt Engineering in Large Language Models},
    author={Daan Kepel, Konstantina Valogianni},
    journal={arXiv preprint arXiv:2407.11000},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11000},
    primaryClass={cs.CL cs.AI cs.HC}
}