218 citations · 297 across the 8 of their papers we have counts for
17 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…
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…
Distribution Matching for Rationalization
Yongfeng Huang, Yujun Chen, Yulun Du +1
The task of rationalization aims to extract pieces of input text as rationales to justify neural network predictions on text classification tasks. By definition, rationales represe…
FastMoE: A Fast Mixture-of-Expert Training System
Jiaao He, Jiezhong Qiu, Aohan Zeng +3
Mixture-of-Expert (MoE) presents a strong potential in enlarging the size of language model to trillions of parameters. However, training trillion-scale MoE requires algorithm and…
Controllable Generation from Pre-trained Language Models via Inverse Prompting
Xu Zou, Da Yin, Qingyang Zhong +4
Large-scale pre-trained language models have demonstrated strong capabilities of generating realistic text. However, it remains challenging to control the generation results. Previ…