Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A
Prompt-Based Uncertainty Propagation Approach

Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach

15 September 2022

Ran Xu

Papers citing "Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach"

8 / 8 papers shown

Title
Towards Informative Few-Shot Prompt with Maximum Information Gain for In-Context Learning Hongfu Liu Ye Wang 23 9 0 13 Oct 2023
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets Guy Hacohen Avihu Dekel D. Weinshall 110 81 0 06 Feb 2022
FLEX: Unifying Evaluation for Few-Shot NLP Jonathan Bragg Arman Cohan Kyle Lo Iz Beltagy 193 103 0 15 Jul 2021
Zero-Round Active Learning Si-An Chen Tianhao Wang R. Jia 40 4 0 14 Jul 2021
What Makes Good In-Context Examples for GPT- $3$ ? Jiachang Liu Dinghan Shen Yizhe Zhang Bill Dolan Lawrence Carin Weizhu Chen AAML RALM 275 1,296 0 17 Jan 2021
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 241 1,898 0 31 Dec 2020
Cold-start Active Learning through Self-supervised Language Modeling Michelle Yuan Hsuan-Tien Lin Jordan L. Boyd-Graber 104 180 0 19 Oct 2020
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference Timo Schick Hinrich Schütze 251 1,584 0 21 Jan 2020