On the Importance of Effectively Adapting Pretrained Language Models for Active Learning

16 April 2021

Papers citing "On the Importance of Effectively Adapting Pretrained Language Models for Active Learning"

11 / 11 papers shown

Title
Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained Language Models Christopher Schröder Gerhard Heyer VLM 44 0 0 13 Jun 2024
AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets Pietro Lesci Andreas Vlachos 35 2 0 08 Apr 2024
STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models Linhai Zhang Jialong Wu Deyu Zhou Guoqiang Xu 30 4 0 02 Mar 2024
Active Learning Principles for In-Context Learning with Large Language Models Katerina Margatina Timo Schick Nikolaos Aletras Jane Dwivedi-Yu 27 39 0 23 May 2023
Rethinking Semi-supervised Learning with Language Models Zhengxiang Shi Francesco Tonolini Nikolaos Aletras Emine Yilmaz G. Kazai Yunlong Jiao 32 18 0 22 May 2023
Investigating Multi-source Active Learning for Natural Language Inference Ard Snijders Douwe Kiela Katerina Margatina 24 7 0 14 Feb 2023
An Efficient Active Learning Pipeline for Legal Text Classification Sepideh Mamooler R. Lebret Stéphane Massonnet Karl Aberer AILaw 21 4 0 15 Nov 2022
A Survey of Active Learning for Natural Language Processing Zhisong Zhang Emma Strubell Eduard H. Hovy LM&MA 33 65 0 18 Oct 2022
Cold-start Active Learning through Self-supervised Language Modeling Michelle Yuan Hsuan-Tien Lin Jordan L. Boyd-Graber 116 180 0 19 Oct 2020
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 297 6,959 0 20 Apr 2018
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015