22 citations · 60 across the 7 of their papers we have counts for
8 papers
Zero-Label Prompt Selection
Chonghua Liao, Yanan Zheng, Zhilin Yang
Natural language prompts have been shown to facilitate cross-task generalization for large language models. However, with no or limited labeled examples, the cross-task performance…
Prompt-Based Metric Learning for Few-Shot NER
Yanru Chen, Yanan Zheng, Zhilin Yang
Few-shot named entity recognition (NER) targets generalizing to unseen labels and/or domains with few labeled examples. Existing metric learning methods compute token-level similar…
A Universal Discriminator for Zero-Shot Generalization
Haike Xu, Zongyu Lin, Jing Zhou +2
Generative modeling has been the dominant approach for large-scale pretraining and zero-shot generalization. In this work, we challenge this convention by showing that discriminati…
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
Xingcheng Yao, Yanan Zheng, Xiaocong Yang +1
Pretrained language models have become the standard approach for many NLP tasks due to strong performance, but they are very expensive to train. We propose a simple and efficient l…
FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding
Yanan Zheng, Jing Zhou, Yujie Qian +7
The few-shot natural language understanding (NLU) task has attracted much recent attention. However, prior methods have been evaluated under a disparate set of protocols, which hin…
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning
Jing Zhou, Yanan Zheng, Jie Tang +2
Most previous methods for text data augmentation are limited to simple tasks and weak baselines. We explore data augmentation on hard tasks (i.e., few-shot natural language underst…
GPT Understands, Too
Xiao Liu, Yanan Zheng, Zhengxiao Du +4
Prompting a pretrained language model with natural language patterns has been proved effective for natural language understanding (NLU). However, our preliminary study reveals that…
CPM: A Large-scale Generative Chinese Pre-trained Language Model
Zhengyan Zhang, Xu Han, Hao Zhou +22
Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3, with 175 billion parameters and 570GB training data, drew a lot o…