Wang Yanggang — accepted AI-conference papers

2022

GPS: Genetic Prompt Search for Efficient Few-Shot Learning

Hanwei Xu, Yujun Chen, Yulun Du, Nan Shao, Wang Yanggang, Haiyu Li +1

EMNLP 2022main

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 (GP…

Cited by 69 Source ↗PDF ↗code ↗Scholar ↗

2022

ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-Shot Generalization

Hanwei Xu, Yujun Chen, Yulun Du, Nan Shao, Wang Yanggang, Haiyu Li +1

EMNLP 2022finding

We propose a multitask pretraining approach ZeroPrompt for zero-shot generalization, focusing on task scaling and zero-shot prompting.While previous models are trained on only a few dozen tasks, we scale to 1,000 tasks for the first time using real-world data. This leads to a crucial discovery that…

Cited by 28 Source ↗PDF ↗Scholar ↗