Scholarlink

<p>The widespread global use of short message service (SMS) has heightened the importance of reliable spam detection, as spam messages are not only disruptive but can also facilitate phishing and identity theft. While extensive prior research has explored SMS spam detection using traditional machine learning (ML), deep learning (DL), and transformer-based encoder models, the recent rapid advances in large language models (LLMs) with strong natural language understanding capabilities motivate a fresh, systematic evaluation. In this study, we investigate how effectively LLMs detect SMS spam using both English and Korean datasets. We benchmark representative prior approaches on recent SMS spam corpora and compare them against LLM-based classifiers. Our experiments cover both commercial black-box LLMs and locally deployable white-box models and examine multiple prompting strategies, including zero-shot, few-shot, and chain-of-thought (CoT) prompting. Furthermore, to reflect adversarial characteristics commonly used to evade detection, we evaluate robustness under concept drift and adversarial attack settings. Experimental results on English and Korean SMS datasets indicate that LLMs are highly robust for spam detection and can provide interpretable rationales by leveraging their language understanding capabilities, suggesting that LLMs are a highly effective and practical option for modern SMS spam filtering.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Are large language models effective for detecting spam messages?

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月16日

查看全文
  • Minseok Hur,
  • Sooyon Seo,
  • Jaeho Hwang,
  • Moohong Min

摘要

The widespread global use of short message service (SMS) has heightened the importance of reliable spam detection, as spam messages are not only disruptive but can also facilitate phishing and identity theft. While extensive prior research has explored SMS spam detection using traditional machine learning (ML), deep learning (DL), and transformer-based encoder models, the recent rapid advances in large language models (LLMs) with strong natural language understanding capabilities motivate a fresh, systematic evaluation. In this study, we investigate how effectively LLMs detect SMS spam using both English and Korean datasets. We benchmark representative prior approaches on recent SMS spam corpora and compare them against LLM-based classifiers. Our experiments cover both commercial black-box LLMs and locally deployable white-box models and examine multiple prompting strategies, including zero-shot, few-shot, and chain-of-thought (CoT) prompting. Furthermore, to reflect adversarial characteristics commonly used to evade detection, we evaluate robustness under concept drift and adversarial attack settings. Experimental results on English and Korean SMS datasets indicate that LLMs are highly robust for spam detection and can provide interpretable rationales by leveraging their language understanding capabilities, suggesting that LLMs are a highly effective and practical option for modern SMS spam filtering.