Tianxiang Sun, Yunfan Shao, Hong Qian, Xuanjing Huang, Xipeng Qiu
TL;DR本文提出黑盒优化框架来通过无导数优化预定义的任务提示,从而在使用预训练语言模型的服务化环境中实现更好的性能。在随机生成的子空间中进行优化,使得黑盒优化框架可以在 RoBERTa 上优化任务提示,并在少量标记样本上显着优于手动提示和 GPT-3 的上下文学习以及梯度优化方法。
Abstract
Extremely large pre-trained language models (PTMs) such as GPT-3 are usually released as a service, allowing users to design task-specific prompts to query the PTMs through some black-box APIs. In such a scenario