@inproceedings{betka-etal-2023-enhancing,
title = "On Enhancing Fine-Tuning for Pre-trained Language Models",
author = "Betka, Abir and
Ferhat, Zeyd and
Barka, Riyadh and
Boutiba, Selma and
Kahhoul, Zineddine and
Lakhdar, Tiar and
Abdelali, Ahmed and
Dahmani, Habiba",
editor = "Sawaf, Hassan and
El-Beltagy, Samhaa and
Zaghouani, Wajdi and
Magdy, Walid and
Abdelali, Ahmed and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Habash, Nizar and
Khalifa, Salam and
Keleg, Amr and
Haddad, Hatem and
Zitouni, Imed and
Mrini, Khalil and
Almatham, Rawan",
booktitle = "Proceedings of ArabicNLP 2023",
month = dec,
year = "2023",
address = "Singapore (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.arabicnlp-1.33",
doi = "10.18653/v1/2023.arabicnlp-1.33",
pages = "405--410",
abstract = "The remarkable capabilities of Natural Language Models to grasp language subtleties has paved the way for their widespread adoption in diverse fields. However, adapting them for specific tasks requires the time-consuming process of fine-tuning, which consumes significant computational power and energy. Therefore, optimizing the fine-tuning time is advantageous. In this study, we propose an alternate approach that limits parameter manipulation to select layers. Our exploration led to identifying layers that offer the best trade-off between time optimization and performance preservation. We further validated this approach on multiple downstream tasks, and the results demonstrated its potential to reduce fine-tuning time by up to 50{\%} while maintaining performance within a negligible deviation of less than 5{\%}. This research showcases a promising technique for significantly improving fine-tuning efficiency without compromising task- or domain-specific learning capabilities.",
}
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<abstract>The remarkable capabilities of Natural Language Models to grasp language subtleties has paved the way for their widespread adoption in diverse fields. However, adapting them for specific tasks requires the time-consuming process of fine-tuning, which consumes significant computational power and energy. Therefore, optimizing the fine-tuning time is advantageous. In this study, we propose an alternate approach that limits parameter manipulation to select layers. Our exploration led to identifying layers that offer the best trade-off between time optimization and performance preservation. We further validated this approach on multiple downstream tasks, and the results demonstrated its potential to reduce fine-tuning time by up to 50% while maintaining performance within a negligible deviation of less than 5%. This research showcases a promising technique for significantly improving fine-tuning efficiency without compromising task- or domain-specific learning capabilities.</abstract>
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<date>2023-12</date>
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%0 Conference Proceedings
%T On Enhancing Fine-Tuning for Pre-trained Language Models
%A Betka, Abir
%A Ferhat, Zeyd
%A Barka, Riyadh
%A Boutiba, Selma
%A Kahhoul, Zineddine
%A Lakhdar, Tiar
%A Abdelali, Ahmed
%A Dahmani, Habiba
%Y Sawaf, Hassan
%Y El-Beltagy, Samhaa
%Y Zaghouani, Wajdi
%Y Magdy, Walid
%Y Abdelali, Ahmed
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Habash, Nizar
%Y Khalifa, Salam
%Y Keleg, Amr
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y Mrini, Khalil
%Y Almatham, Rawan
%S Proceedings of ArabicNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore (Hybrid)
%F betka-etal-2023-enhancing
%X The remarkable capabilities of Natural Language Models to grasp language subtleties has paved the way for their widespread adoption in diverse fields. However, adapting them for specific tasks requires the time-consuming process of fine-tuning, which consumes significant computational power and energy. Therefore, optimizing the fine-tuning time is advantageous. In this study, we propose an alternate approach that limits parameter manipulation to select layers. Our exploration led to identifying layers that offer the best trade-off between time optimization and performance preservation. We further validated this approach on multiple downstream tasks, and the results demonstrated its potential to reduce fine-tuning time by up to 50% while maintaining performance within a negligible deviation of less than 5%. This research showcases a promising technique for significantly improving fine-tuning efficiency without compromising task- or domain-specific learning capabilities.
%R 10.18653/v1/2023.arabicnlp-1.33
%U https://aclanthology.org/2023.arabicnlp-1.33
%U https://doi.org/10.18653/v1/2023.arabicnlp-1.33
%P 405-410
Markdown (Informal)
[On Enhancing Fine-Tuning for Pre-trained Language Models](https://aclanthology.org/2023.arabicnlp-1.33) (Betka et al., ArabicNLP-WS 2023)
ACL
- Abir Betka, Zeyd Ferhat, Riyadh Barka, Selma Boutiba, Zineddine Kahhoul, Tiar Lakhdar, Ahmed Abdelali, and Habiba Dahmani. 2023. On Enhancing Fine-Tuning for Pre-trained Language Models. In Proceedings of ArabicNLP 2023, pages 405–410, Singapore (Hybrid). Association for Computational Linguistics.