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Do prompt positions really matter?

Do prompt positions really matter?

23 May 2023
Junyu Mao
Stuart E. Middleton
Mahesan Niranjan
    VLM
ArXivPDFHTML

Papers citing "Do prompt positions really matter?"

7 / 7 papers shown
Title
Dynamic Prompting: A Unified Framework for Prompt Tuning
Dynamic Prompting: A Unified Framework for Prompt Tuning
Xianjun Yang
Wei Cheng
Xujiang Zhao
Wenchao Yu
Linda R. Petzold
Haifeng Chen
VLM
17
14
0
06 Mar 2023
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
Xiao Liu
Kaixuan Ji
Yicheng Fu
Weng Lam Tam
Zhengxiao Du
Zhilin Yang
Jie Tang
VLM
236
780
0
14 Oct 2021
Fantastically Ordered Prompts and Where to Find Them: Overcoming
  Few-Shot Prompt Order Sensitivity
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity
Yao Lu
Max Bartolo
Alastair Moore
Sebastian Riedel
Pontus Stenetorp
AILaw
LRM
274
1,114
0
18 Apr 2021
The Power of Scale for Parameter-Efficient Prompt Tuning
The Power of Scale for Parameter-Efficient Prompt Tuning
Brian Lester
Rami Al-Rfou
Noah Constant
VPVLM
278
3,784
0
18 Apr 2021
Making Pre-trained Language Models Better Few-shot Learners
Making Pre-trained Language Models Better Few-shot Learners
Tianyu Gao
Adam Fisch
Danqi Chen
241
1,898
0
31 Dec 2020
Exploiting Cloze Questions for Few Shot Text Classification and Natural
  Language Inference
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference
Timo Schick
Hinrich Schütze
251
1,584
0
21 Jan 2020
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language
  Understanding
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
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