1.8k citations · 3.8k across the 47 of their papers we have counts for
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Learning to Detect Noisy Labels Using Model-Based Features
Zhihao Wang, Zongyu Lin, Peiqi Liu +5
Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation. Many existing approaches are based on he…
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…
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…