GPS: Genetic Prompt Search for Efficient Few-Shot Learning
Hanwei Xu, Yujun Chen, Yulun Du, Nan Shao, Wang Yanggang, Haiyu Li, Zhilin Yang
Abstract
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 requiring 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 the best prompt.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.- Anthology ID:
- 2022.emnlp-main.559
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8162–8171
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.559
- DOI:
- Cite (ACL):
- Hanwei Xu, Yujun Chen, Yulun Du, Nan Shao, Wang Yanggang, Haiyu Li, and Zhilin Yang. 2022. GPS: Genetic Prompt Search for Efficient Few-Shot Learning. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8162–8171, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- GPS: Genetic Prompt Search for Efficient Few-Shot Learning (Xu et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.emnlp-main.559.pdf