Can You Label Less by Using Out-of-Domain Data? Active & Transfer
Learning with Few-shot Instructions

Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions

21 November 2022

Rafal Kocielnik

Sara Kangaslahti

Shrimai Prabhumoye

Anima Anandkumar

Papers citing "Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions"

6 / 6 papers shown

Title
Active Learning Principles for In-Context Learning with Large Language Models Katerina Margatina Timo Schick Nikolaos Aletras Jane Dwivedi-Yu 30 39 0 23 May 2023
Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach Yue Yu Rongzhi Zhang Ran Xu Jieyu Zhang Jiaming Shen Chao Zhang 50 21 0 15 Sep 2022
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER Dong-Ho Lee Akshen Kadakia Kangmin Tan Mahak Agarwal Xinyu Feng Takashi Shibuya Ryosuke Mitani Toshiyuki Sekiya Jay Pujara Xiang Ren 40 84 0 16 Oct 2021
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP Timo Schick Sahana Udupa Hinrich Schütze 259 374 0 28 Feb 2021
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 241 1,919 0 31 Dec 2020
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism M. Shoeybi M. Patwary Raul Puri P. LeGresley Jared Casper Bryan Catanzaro MoE 245 1,821 0 17 Sep 2019