Combating Phone Scams with LLM-based Detection: Where Do We Stand?
It addresses phone scams for individuals and communities, but is incremental as it builds on existing LLM methods for a specific domain.
This research tackles phone scam detection by exploring large language models (LLMs) to analyze conversational dynamics, showing promising results but facing challenges like biased datasets and low recall.
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