@Article{JML-2-4,
author = {Yifan, Luo and Yiming, Tang and Shen, Chengfeng and Zhou, Zhenan and Dong, Bin},
title = {Prompt Engineering Through the Lens of Optimal Control},
journal = {Journal of Machine Learning},
year = {2023},
volume = {2},
number = {4},
pages = {241--258},
abstract = {<p style="text-align: justify;">Prompt engineering (PE) has emerged as a critical technique for guiding large language models
(LLMs) in solving intricate tasks. Its importance is highlighted by its potential to significantly enhance the efficiency and effectiveness of human-machine interaction. As tasks grow increasingly complex, recent advanced
PE methods have extended beyond the limitations of single-round interactions to embrace multi-round interactions, which allows for a deeper and more nuanced engagement with LLMs. In this paper, we propose
an optimal control framework tailored for multi-round interactions with LLMs. This framework provides
a unified mathematical structure that not only systematizes the existing PE methods but also sets the stage
for rigorous analytical improvements. Furthermore, we extend this framework to include PE via ensemble
methods and multi-agent collaboration, thereby enlarging the scope of applicability. By adopting an optimal control perspective, we offer fresh insights into existing PE methods and highlight theoretical challenges
that warrant future research. Besides, our work lays a foundation for the development of more effective and
interpretable PE methods.</p>},
issn = {2790-2048},
doi = {https://doi.org/10.4208/jml.231023},
url = {https://global-sci.com/article/87603/prompt-engineering-through-the-lens-of-optimal-control}
}