@inproceedings{zhou-etal-2023-revisiting,
title = "Revisiting Automated Prompting: Are We Actually Doing Better?",
author = "Zhou, Yulin and
Zhao, Yiren and
Shumailov, Ilia and
Mullins, Robert and
Gal, Yarin",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.155",
doi = "10.18653/v1/2023.acl-short.155",
pages = "1822--1832",
abstract = "Current literature demonstrates that Large Language Models (LLMs) are great few-shot learners, and prompting significantly increases their performance on a range of downstream tasks in a few-shot learning setting. An attempt to automate human-led prompting followed, with some progress achieved. In particular, subsequent work demonstrates that automation can outperform fine-tuning in certain K-shot learning scenarios. In this paper, we revisit techniques for automated prompting on six different downstream tasks and a larger range of K-shot learning settings. We find that automated prompting does not consistently outperform simple manual prompting. Our work suggests that, in addition to fine-tuning, manual prompting should be used as a baseline in this line of research.",
}
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<abstract>Current literature demonstrates that Large Language Models (LLMs) are great few-shot learners, and prompting significantly increases their performance on a range of downstream tasks in a few-shot learning setting. An attempt to automate human-led prompting followed, with some progress achieved. In particular, subsequent work demonstrates that automation can outperform fine-tuning in certain K-shot learning scenarios. In this paper, we revisit techniques for automated prompting on six different downstream tasks and a larger range of K-shot learning settings. We find that automated prompting does not consistently outperform simple manual prompting. Our work suggests that, in addition to fine-tuning, manual prompting should be used as a baseline in this line of research.</abstract>
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%0 Conference Proceedings
%T Revisiting Automated Prompting: Are We Actually Doing Better?
%A Zhou, Yulin
%A Zhao, Yiren
%A Shumailov, Ilia
%A Mullins, Robert
%A Gal, Yarin
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhou-etal-2023-revisiting
%X Current literature demonstrates that Large Language Models (LLMs) are great few-shot learners, and prompting significantly increases their performance on a range of downstream tasks in a few-shot learning setting. An attempt to automate human-led prompting followed, with some progress achieved. In particular, subsequent work demonstrates that automation can outperform fine-tuning in certain K-shot learning scenarios. In this paper, we revisit techniques for automated prompting on six different downstream tasks and a larger range of K-shot learning settings. We find that automated prompting does not consistently outperform simple manual prompting. Our work suggests that, in addition to fine-tuning, manual prompting should be used as a baseline in this line of research.
%R 10.18653/v1/2023.acl-short.155
%U https://aclanthology.org/2023.acl-short.155
%U https://doi.org/10.18653/v1/2023.acl-short.155
%P 1822-1832
Markdown (Informal)
[Revisiting Automated Prompting: Are We Actually Doing Better?](https://aclanthology.org/2023.acl-short.155) (Zhou et al., ACL 2023)
ACL
- Yulin Zhou, Yiren Zhao, Ilia Shumailov, Robert Mullins, and Yarin Gal. 2023. Revisiting Automated Prompting: Are We Actually Doing Better?. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1822–1832, Toronto, Canada. Association for Computational Linguistics.