利用基于LLM的检测来打击电话诈骗：我们现在进展如何？
Combating Phone Scams with LLM-based Detection: Where Do We Stand?
AI总结：
本研究探讨了利用大型语言模型分析对话动态以实时检测欺诈电话的潜力，证实其能为用户提供即时保护，同时指出偏见数据集、低召回率和幻觉等问题亟待解决。
摘要：
电话诈骗对个人和社区构成重大威胁，造成巨大的经济损失和情感困扰。尽管不断努力打击这些诈骗，骗子仍不断适应和改进其策略，因此探索创新的对策势在必行。本研究探讨了大型语言模型（LLMs）提供欺诈电话检测的潜力。通过分析骗子与受害者之间的对话动态，基于LLM的检测器可以在诈骗发生时识别潜在诈骗，为用户提供即时保护。虽然这些方法展示了有前景的结果，我们也承认存在偏见数据集、相对较低的召回率和幻觉等挑战，必须解决这些问题以推动该领域的进一步发展。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
