ML Anthology

Combating Phone Scams with LLM-Based Detection: Where Do We Stand? (Student Abstract)

Zitong Shen, Kangzhong Wang, Youqian Zhang, Grace Ngai, Eugene Yujun Fu
AAAI 2025 pp. 29487-29489
doi:10.1609/AAAI.V39I28.35298 /aaai/2025/shen2025aaai-combating/

Abstract

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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Cite

Text

Shen et al. "Combating Phone Scams with LLM-Based Detection: Where Do We Stand? (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35298

Markdown

[Shen et al. "Combating Phone Scams with LLM-Based Detection: Where Do We Stand? (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/shen2025aaai-combating/) doi:10.1609/AAAI.V39I28.35298

BibTeX

@inproceedings{shen2025aaai-combating,
  title     = {{Combating Phone Scams with LLM-Based Detection: Where Do We Stand? (Student Abstract)}},
  author    = {Shen, Zitong and Wang, Kangzhong and Zhang, Youqian and Ngai, Grace and Fu, Eugene Yujun},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {29487-29489},
  doi       = {10.1609/AAAI.V39I28.35298},
  url       = {https://mlanthology.org/aaai/2025/shen2025aaai-combating/}
}