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        <title>Extreme Fine-tuning: A Novel and Fast Fine-tuning Approach for Text Classification</title>
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            <namePart type="given">Yvette</namePart>
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    <abstract>Although fine-tuning a pre-trained model with a conventional approach has shown to be effective in various downstream tasks, previous work has used only backpropagation to fine-tune the model, which causes a massive amount of computational resources and time. We propose Extreme Fine-Tuning (EFT), a novel approach for fine-tuning a pre-trained model effectively and efficiently. EFT uses backpropagation for a brief fine-tuning and an iterative extreme learning machine for training a classifier. We applied EFT to four text classification datasets, MELD, IEMOCAP, IMDb, and AG News, and compared its performance with state-of-the-art (SOTA) approaches. The results indicate that EFT noticeably outperformed the other approaches in training-time measurement with comparable model performance. We will release our code at https://github.com/up-33/extreme-fine-tuning.</abstract>
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%0 Conference Proceedings
%T Extreme Fine-tuning: A Novel and Fast Fine-tuning Approach for Text Classification
%A Jiaramaneepinit, Boonnithi
%A Chay-intr, Thodsaporn
%A Funakoshi, Kotaro
%A Okumura, Manabu
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F jiaramaneepinit-etal-2024-extreme
%X Although fine-tuning a pre-trained model with a conventional approach has shown to be effective in various downstream tasks, previous work has used only backpropagation to fine-tune the model, which causes a massive amount of computational resources and time. We propose Extreme Fine-Tuning (EFT), a novel approach for fine-tuning a pre-trained model effectively and efficiently. EFT uses backpropagation for a brief fine-tuning and an iterative extreme learning machine for training a classifier. We applied EFT to four text classification datasets, MELD, IEMOCAP, IMDb, and AG News, and compared its performance with state-of-the-art (SOTA) approaches. The results indicate that EFT noticeably outperformed the other approaches in training-time measurement with comparable model performance. We will release our code at https://github.com/up-33/extreme-fine-tuning.
%U https://aclanthology.org/2024.eacl-short.32
%P 368-379

Markdown (Informal)

[Extreme Fine-tuning: A Novel and Fast Fine-tuning Approach for Text Classification](https://aclanthology.org/2024.eacl-short.32) (Jiaramaneepinit et al., EACL 2024)

• Extreme Fine-tuning: A Novel and Fast Fine-tuning Approach for Text Classification (Jiaramaneepinit et al., EACL 2024)

ACL

• Boonnithi Jiaramaneepinit, Thodsaporn Chay-intr, Kotaro Funakoshi, and Manabu Okumura. 2024. Extreme Fine-tuning: A Novel and Fast Fine-tuning Approach for Text Classification. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 368–379, St. Julian’s, Malta. Association for Computational Linguistics.