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P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking
  Fine-tuning with Prompt-based Learning and Pre-finetuning

P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning

4 May 2022
Xiaomeng Hu
S. Yu
Chenyan Xiong
Zhenghao Liu
Zhiyuan Liu
Geoffrey X. Yu
ArXivPDFHTML

Papers citing "P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning"

4 / 4 papers shown
Title
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
280
3,843
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
1,916
0
31 Dec 2020
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision
Si Sun
Yingzhuo Qian
Zhenghao Liu
Chenyan Xiong
Kaitao Zhang
Jie Bao
Zhiyuan Liu
Paul N. Bennett
36
18
0
29 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
258
1,586
0
21 Jan 2020
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