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        <title>Small is the New Big: Pre-finetuned compact models are better for Asynchronous Active Learning</title>
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%0 Conference Proceedings
%T Small is the New Big: Pre-finetuned compact models are better for Asynchronous Active Learning
%A Liu, Dantong
%A Pavani, Kaushik
%A Dasgupta, Sunny
%Y Sadat Moosavi, Nafise
%Y Gurevych, Iryna
%Y Hou, Yufang
%Y Kim, Gyuwan
%Y Kim, Young Jin
%Y Schuster, Tal
%Y Agrawal, Ameeta
%S Proceedings of The Fourth Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada (Hybrid)
%F liu-etal-2023-small
%R 10.18653/v1/2023.sustainlp-1.7
%U https://aclanthology.org/2023.sustainlp-1.7
%U https://doi.org/10.18653/v1/2023.sustainlp-1.7
%P 110-120

Markdown (Informal)

[Small is the New Big: Pre-finetuned compact models are better for Asynchronous Active Learning](https://aclanthology.org/2023.sustainlp-1.7) (Liu et al., sustainlp 2023)

• Small is the New Big: Pre-finetuned compact models are better for Asynchronous Active Learning (Liu et al., sustainlp 2023)

ACL

• Dantong Liu, Kaushik Pavani, and Sunny Dasgupta. 2023. Small is the New Big: Pre-finetuned compact models are better for Asynchronous Active Learning. In Proceedings of The Fourth Workshop on Simple and Efficient Natural Language Processing (SustaiNLP), pages 110–120, Toronto, Canada (Hybrid). Association for Computational Linguistics.