| Title: |
Combating Phone Scams with LLM-based Detection: Where Do We Stand? |
| Authors: |
Shen, Zitong, Wang, Kangzhong, Zhang, Youqian, Ngai, Grace, Fu, Eugene Y. |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, I.2.0 |
| More Details: |
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
Comment: 2 pages, 1 figure |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2409.11643 |
| Accession Number: |
edsarx.2409.11643 |
| Database: |
arXiv |
