This repo contains the code of the discrete prompt optimization framework described in the paper \ RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning \ Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh* (equal contribution), Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P. Xing, Zhiting Hu
We will keep updating the codebase for easier usage and adaptation for your own tasks, so please stay tuned by starring or watching our repo!
Our codebase requires the following Python and PyTorch versions:
Install our core modules with
pip install -e .
Please refer to the folders in examples
, which contains our implementations of 1) few-shot classification and 2) text style transfer, as described in our paper.
In short, the code in rlprompt
provides the core components for prompt optimization. The task-specific folders in examples
simply implement the reward functions and use the core modules to run experiments.