P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally
Across Scales and Tasks

P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

14 October 2021

Xiao Liu

Papers citing "P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks"

7 / 7 papers shown

Title
MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators Zhixing Tan Xiangwen Zhang Shuo Wang Yang Liu VLM LRM 176 45 0 13 Oct 2021
Aspect Sentiment Quad Prediction as Paraphrase Generation Wenxuan Zhang Yang Deng Xin Li Yifei Yuan Lidong Bing W. Lam 176 127 0 02 Oct 2021
FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding Yanan Zheng Jing Zhou Yujie Qian Ming Ding Chonghua Liao Jian Li Ruslan Salakhutdinov Jie Tang Sebastian Ruder Zhilin Yang ELM 177 28 0 27 Sep 2021
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers Boseop Kim Hyoungseok Kim Sang-Woo Lee Gichang Lee Donghyun Kwak ... Jaewook Kang Inho Kang Jung-Woo Ha W. Park Nako Sung VLM 204 108 0 10 Sep 2021
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 254 2,999 0 18 Apr 2021
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 223 1,649 0 31 Dec 2020
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 267 6,003 0 20 Apr 2018