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Model ensemble instead of prompt fusion: a sample-specific knowledge
  transfer method for few-shot prompt tuning

Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning

23 October 2022
Xiangyu Peng
Chen Xing
Prafulla Kumar Choubey
Chien-Sheng Wu
Caiming Xiong
    VLM
ArXivPDFHTML

Papers citing "Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning"

5 / 5 papers shown
Title
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer
Tu Vu
Brian Lester
Noah Constant
Rami Al-Rfou
Daniel Matthew Cer
VLM
LRM
131
276
0
15 Oct 2021
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
234
780
0
14 Oct 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
275
3,784
0
18 Apr 2021
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
248
1,382
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
1