KorSmishing Explainer: A Korean-centric LLM-based Framework for Smishing Detection and Explanation Generation
Yunseung Lee, Daehee Han · 2024
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 parameterefficient 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.