38 citations · 54 across the 5 of their papers we have counts for
5 papers
GPS: Genetic Prompt Search for Efficient Few-shot Learning
Hanwei Xu, Yujun Chen, Yulun Du +4
Prompt-based techniques have demostrated great potential for improving the few-shot generalization of pretrained language models. However, their performance heavily relies on the m…
ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-Shot Generalization
Hanwei Xu, Yujun Chen, Yulun Du +4
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 f…
Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering
Nan Shao, Yiming Cui, Ting Liu +2
Retrieving information from correlative paragraphs or documents to answer open-domain multi-hop questions is very challenging. To deal with this challenge, most of the existing wor…
Is Graph Structure Necessary for Multi-hop Question Answering?
Nan Shao, Yiming Cui, Ting Liu +2
Recently, attempting to model texts as graph structure and introducing graph neural networks to deal with it has become a trend in many NLP research areas. In this paper, we invest…
TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-based Chatbots
Wentao Ma, Yiming Cui, Nan Shao +5
We consider the importance of different utterances in the context for selecting the response usually depends on the current query. In this paper, we propose the model TripleNet to…