@inproceedings{ma-etal-2025-detecting-continuously,
    title = "Detecting Continuously Evolving Scam Calls under Limited Annotation: A {LLM}-Augmented Expert Rule Framework",
    author = "Ma, Haoyu  and
      Su, Qinliang  and
      Huang, Minhua  and
      Kai, Wu",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.270/",
    doi = "10.18653/v1/2025.findings-emnlp.270",
    pages = "5047--5068",
    ISBN = "979-8-89176-335-7",
    abstract = "The increasing prevalence of scam calls, particularly on online platforms for recruitment, ride-hailing, and delivery services, has become a significant social and economic issue. Traditional approaches to scam call detection rely on labeled data and assume a static distribution of scam narratives. However, scammers continuously evolve their tactics, making these methods less effective. In this paper, we propose a novel approach leveraging large language models (LLMs) to detect continuously evolving scam calls. By abstracting scam and normal call rules based on expert knowledge, we develop a hierarchical few-shot prompting framework. This framework consists of a discrimination module to identify scam characteristics, a reflection module to reduce false positives by comparing with normal call features, and a summary step to synthesize the final detection results. Our method is evaluated on real-world and synthesized datasets, demonstrating superior performance in detecting evolving scam calls with minimal labeled data. Furthermore, we show that the framework is highly adaptable to new scam detection scenarios, requiring only modifications to the expert rules."
}