@inproceedings{gagiano-tian-2023-prompt,
title = "A Prompt in the Right Direction: Prompt Based Classification of Machine-Generated Text Detection",
author = "Gagiano, Rinaldo and
Tian, Lin",
editor = "Muresan, Smaranda and
Chen, Vivian and
Casey, Kennington and
David, Vandyke and
Nina, Dethlefs and
Koji, Inoue and
Erik, Ekstedt and
Stefan, Ultes",
booktitle = "Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association",
month = nov,
year = "2023",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.alta-1.18",
pages = "153--158",
abstract = "The goal of ALTA 2023 Shared Task is to distinguish between human-authored text and synthetic text generated by Large Language Models (LLMs). Given the growing societal concerns surrounding LLMs, this task addresses the urgent need for robust text verification strategies. In this paper, we describe our method, a fine-tuned Falcon-7B model with incorporated label smoothing into the training process. We applied model prompting to samples with lower confidence scores to enhance prediction accuracy. Our model achieved a statistically significant accuracy of 0.991.",
}
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%0 Conference Proceedings
%T A Prompt in the Right Direction: Prompt Based Classification of Machine-Generated Text Detection
%A Gagiano, Rinaldo
%A Tian, Lin
%Y Muresan, Smaranda
%Y Chen, Vivian
%Y Casey, Kennington
%Y David, Vandyke
%Y Nina, Dethlefs
%Y Koji, Inoue
%Y Erik, Ekstedt
%Y Stefan, Ultes
%S Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association
%D 2023
%8 November
%I Association for Computational Linguistics
%C Melbourne, Australia
%F gagiano-tian-2023-prompt
%X The goal of ALTA 2023 Shared Task is to distinguish between human-authored text and synthetic text generated by Large Language Models (LLMs). Given the growing societal concerns surrounding LLMs, this task addresses the urgent need for robust text verification strategies. In this paper, we describe our method, a fine-tuned Falcon-7B model with incorporated label smoothing into the training process. We applied model prompting to samples with lower confidence scores to enhance prediction accuracy. Our model achieved a statistically significant accuracy of 0.991.
%U https://aclanthology.org/2023.alta-1.18
%P 153-158
Markdown (Informal)
[A Prompt in the Right Direction: Prompt Based Classification of Machine-Generated Text Detection](https://aclanthology.org/2023.alta-1.18) (Gagiano & Tian, ALTA 2023)
ACL