:::info
Authors:
(1) Chengrun Yang, Google DeepMind and Equal contribution;
(2) Xuezhi Wang, Google DeepMind;
(3) Yifeng Lu, Google DeepMind;
(4) Hanxiao Liu, Google DeepMind;
(5) Quoc V. Le, Google DeepMind;
(6) Denny Zhou, Google DeepMind;
(7) Xinyun Chen, Google DeepMind and Equal contribution.
:::
Table of Links
Abstract and 1. Introduction
2 Opro: Llm as the Optimizer and 2.1 Desirables of Optimization by Llms
2.2 Meta-Prompt Design
3 Motivating Example: Mathematical Optimization and 3.1 Linear Regression
3.2 Traveling Salesman Problem (TSP)
4 Application: Prompt Optimization and 4.1 Problem Setup
4.2 Meta-Prompt Design
5 Prompt Optimization Experiments and 5.1 Evaluation Setup
5.2 Main Results
5.3 Ablation Studies
5.4 Overfitting Analysis in Prompt Optimization and 5.5 Comparison with Evoprompt
6 Related Work
7 Conclusion, Acknowledgments and References
A Some Failure Cases
B Prompting Formats for Scorer Llm
C Meta-Prompts and C.1 Meta-Prompt for Math Optimization
C.2 Meta-Prompt for Prompt Optimization
D Prompt Optimization Curves on the Remaining Bbh Tasks
E Prompt Optimization on Bbh Tasks – Tabulated Accuracies and Found Instructions
C META-PROMPTS
C.1 META-PROMPT FOR MATH OPTIMIZATION
:::info
This paper is available on arxiv under CC0 1.0 DEED license.
:::
