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

Zhiyuan Liu

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

5 / 5 papers shown

Title
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection Sophia Althammer Guido Zuccon Sebastian Hofstatter Suzan Verberne Allan Hanbury 34 4 0 12 Sep 2023
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 280 3,858 0 18 Apr 2021
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 243 1,924 0 31 Dec 2020
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 Timo Schick Hinrich Schütze 258 1,589 0 21 Jan 2020