Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts

Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts

20 October 2022

Xiangyang Liu

Tianxiang Sun

Xuanjing Huang

Xipeng Qiu

Papers citing "Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts"

8 / 8 papers shown

Title
Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies L. Wang Sheng Chen Linnan Jiang Shu Pan Runze Cai Sen Yang Fei Yang 44 3 0 24 Oct 2024
LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learners Yaqing Wang Subhabrata Mukherjee Xiaodong Liu Jing Gao Ahmed Hassan Awadallah Jianfeng Gao VLM BDL 38 10 0 12 Oct 2021
Paradigm Shift in Natural Language Processing Tianxiang Sun Xiangyang Liu Xipeng Qiu Xuanjing Huang 109 82 0 26 Sep 2021
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 278 3,784 0 18 Apr 2021
Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters Aston Zhang Yi Tay Shuai Zhang Alvin Chan A. Luu S. Hui Jie Fu MQ 163 83 0 17 Feb 2021
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 241 1,898 0 31 Dec 2020
Pre-trained Models for Natural Language Processing: A Survey Xipeng Qiu Tianxiang Sun Yige Xu Yunfan Shao Ning Dai Xuanjing Huang LM&MA VLM 229 1,281 0 18 Mar 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 294 6,927 0 20 Apr 2018