Prompt-based techniques have demostrated great potential for improving the few-shot generalization of pretrained language models. However, their performance heavily relies on the manual design of prompts and thus requires a lot of human efforts. In this paper, we introduce Genetic Prompt Search (GPS) to improve few-shot learning with prompts, which utilizes a genetic algorithm to automatically search for high-performing prompts. GPS is gradient-free and requires no update of model parameters but only a small validation set. Experiments on diverse datasets proved the effectiveness of GPS, which outperforms manual prompts by a large margin of 2.6 points. Our method is also better than other parameter-efficient tuning methods such as prompt tuning.
@article{arxiv.2210.17041,
title = {GPS: Genetic Prompt Search for Efficient Few-shot Learning},
author = {Hanwei Xu and Yujun Chen and Yulun Du and Nan Shao and Yanggang Wang and Haiyu Li and Zhilin Yang},
journal= {arXiv preprint arXiv:2210.17041},
year = {2022}
}