Dialogue for Prompting: A Policy-Gradient-Based Discrete Prompt Optimization for Few-shot Learning

This repository contains code for Dialogue for Prompting: A Policy-Gradient-Based Discrete Prompt Optimization for Few-shot Learning (https://arxiv.org/abs/2308.07272, AAAI 2024) by Chengzhengxu Li, Xiaoming Liu*, Yichen Wang, Duyi Li, Yu Lan, Chao Shen. In this codebase we provide DP2O, a novel discrete prompt optimization method for few-shot learning. DP2O significantly improves the performance of PLMs in various downstream tasks while ensuring prompt readability and transferability. In subsequent analysis , we also verify DP2O’s good universality, robustness, generalization ability, lightweight and efficiency.

Setting Up

Our codebase requires the following Python and PyTorch versions:

Python >= 3.8
PyTorch >= 1.8.1 (install from the official website)

Install our core modules with

git clone https://github.com/czx-li/DP2O.git

Train and save our modules

python main.py

Citation

If you find our work helpful, please cite us with the following BibTex entry:

@inproceedings{li2024dialogue,
  title={Dialogue for Prompting: A Policy-Gradient-Based Discrete Prompt Generation for Few-Shot Learning},
  author={Li, Chengzhengxu and Liu, Xiaoming and Wang, Yichen and Li, Duyi and Lan, Yu and Shen, Chao},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={16},
  pages={18481--18489},
  year={2024}
}

Link to AAAI 2024 version paper: https://ojs.aaai.org/index.php/AAAI/article/view/29809