@inproceedings{lee-han-2024-korsmishing,
title = "{K}or{S}mishing Explainer: A {K}orean-centric {LLM}-based Framework for Smishing Detection and Explanation Generation",
author = "Lee, Yunseung and
Han, Daehee",
editor = "Dernoncourt, Franck and
Preo{\c{t}}iuc-Pietro, Daniel and
Shimorina, Anastasia",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2024",
address = "Miami, Florida, US",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-industry.47",
pages = "642--656",
abstract = "To mitigate the annual financial losses caused by SMS phishing (smishing) in South Korea, we propose an explainable smishing detection framework that adapts to a Korean-centric large language model (LLM). Our framework not only classifies smishing attempts but also provides clear explanations, enabling users to identify and understand these threats. This end-to-end solution encompasses data collection, pseudo-label generation, and parameter-efficient task adaptation for models with fewer than five billion parameters. Our approach achieves a 15{\%} improvement in accuracy over GPT-4 and generates high-quality explanatory text, as validated by seven automatic metrics and qualitative evaluation, including human assessments.",
}
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%0 Conference Proceedings
%T KorSmishing Explainer: A Korean-centric LLM-based Framework for Smishing Detection and Explanation Generation
%A Lee, Yunseung
%A Han, Daehee
%Y Dernoncourt, Franck
%Y Preoţiuc-Pietro, Daniel
%Y Shimorina, Anastasia
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, US
%F lee-han-2024-korsmishing
%X To mitigate the annual financial losses caused by SMS phishing (smishing) in South Korea, we propose an explainable smishing detection framework that adapts to a Korean-centric large language model (LLM). Our framework not only classifies smishing attempts but also provides clear explanations, enabling users to identify and understand these threats. This end-to-end solution encompasses data collection, pseudo-label generation, and parameter-efficient task adaptation for models with fewer than five billion parameters. Our approach achieves a 15% improvement in accuracy over GPT-4 and generates high-quality explanatory text, as validated by seven automatic metrics and qualitative evaluation, including human assessments.
%U https://aclanthology.org/2024.emnlp-industry.47
%P 642-656
Markdown (Informal)
[KorSmishing Explainer: A Korean-centric LLM-based Framework for Smishing Detection and Explanation Generation](https://aclanthology.org/2024.emnlp-industry.47) (Lee & Han, EMNLP 2024)
ACL