用大模型实时识别诈骗电话，提升防护能力
Combating Phone Scams with LLM-based Detection: Where Do We Stand?
- 基于对话动态分析，用大模型实时检测诈骗通话
- 已实现初步检测效果，但召回率仍较低
- 适合安全防护、反诈系统研发人员参考
电话诈骗对个人与社会构成重大威胁，造成巨大经济损失与情感伤害。尽管持续投入应对，诈骗手法仍在不断演变，亟需创新防御手段。本研究探索大型语言模型（LLMs）在识别诈骗电话中的潜力。通过分析诈骗者与受害者之间的对话动态，基于LLM的检测器可在通话过程中即时识别潜在诈骗，为用户提供实时保护。尽管该方法展现出良好前景，但仍面临数据集偏差、召回率偏低及模型幻觉等挑战，需进一步解决以推动该领域发展。
原文摘要 · Abstract (English)
Phone scams pose a significant threat to individuals and communities, causing substantial financial losses and emotional distress. Despite ongoing efforts to combat these scams, scammers continue to adapt and refine their tactics, making it imperative to explore innovative countermeasures. This research explores the potential of large language models (LLMs) to provide detection of fraudulent phone calls. By analyzing the conversational dynamics between scammers and victims, LLM-based detectors can identify potential scams as they occur, offering immediate protection to users. While such approaches demonstrate promising results, we also acknowledge the challenges of biased datasets, relatively low recall, and hallucinations that must be addressed for further advancement in this field
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