Exploring Lottery Prompts for Pre-trained Language Models

Exploring Lottery Prompts for Pre-trained Language Models

31 May 2023

Zhiyuan Liu

Papers citing "Exploring Lottery Prompts for Pre-trained Language Models"

8 / 8 papers shown

Title
Situated Natural Language Explanations Zining Zhu Hao Jiang Jingfeng Yang Sreyashi Nag Chao Zhang Jie Huang Yifan Gao Frank Rudzicz Bing Yin LRM 44 1 0 27 Aug 2023
Multitask Prompted Training Enables Zero-Shot Task Generalization Victor Sanh Albert Webson Colin Raffel Stephen H. Bach Lintang Sutawika ... T. Bers Stella Biderman Leo Gao Thomas Wolf Alexander M. Rush LRM 213 1,657 0 15 Oct 2021
P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks Xiao Liu Kaixuan Ji Yicheng Fu Weng Lam Tam Zhengxiao Du Zhilin Yang Jie Tang VLM 238 806 0 14 Oct 2021
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 280 3,848 0 18 Apr 2021
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 241 1,919 0 31 Dec 2020
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference Timo Schick Hinrich Schütze 258 1,588 0 21 Jan 2020
Language Models as Knowledge Bases? Fabio Petroni Tim Rocktaschel Patrick Lewis A. Bakhtin Yuxiang Wu Alexander H. Miller Sebastian Riedel KELM AI4MH 415 2,586 0 03 Sep 2019
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 297 6,959 0 20 Apr 2018