GPS: Genetic Prompt Search for Efficient Few-Shot Learning
Hanwei Xu, Yujun Chen, Yulun Du, Nan Shao, Wang Yanggang, Haiyu Li, Zhilin Yang · 2022
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.