@inproceedings{li-2025-knowledge,
    title = "Knowledge-Grounded Detection of Cryptocurrency Scams with Retrieval-Augmented {LM}s",
    author = "Li, Zichao",
    editor = "Zhang, Yuji  and
      Chen, Canyu  and
      Li, Sha  and
      Geva, Mor  and
      Han, Chi  and
      Wang, Xiaozhi  and
      Feng, Shangbin  and
      Gao, Silin  and
      Augenstein, Isabelle  and
      Bansal, Mohit  and
      Li, Manling  and
      Ji, Heng",
    booktitle = "Proceedings of the 3rd Workshop on Towards Knowledgeable Foundation Models (KnowFM)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.knowllm-1.4/",
    pages = "40--48",
    ISBN = "979-8-89176-283-1",
    abstract = "This paper presents a knowledge-grounded framework for cryptocurrency scam detection using retrieval-augmented language models. We address three key limitations of existing approaches: static knowledge bases, unreliable LM outputs, and fixed classification thresholds. Our method combines (1) temporally-weighted retrieval from scam databases, (2) confidence-aware fusion of parametric and external knowledge, and (3) adaptive threshold optimization via gradient ascent. Experiments on CryptoScams and Twitter Financial Scams datasets demonstrate state-of-the-art performance, with 22{\%} higher recall at equivalent precision compared to fixed thresholds, 4.3{\texttimes} lower hallucination rates than pure LMs, and 89{\%} temporal performance retention on emerging scam types. The system achieves real-time operation (45ms/query) while maintaining interpretability through evidence grounding. Ablation studies confirm each component{'}s necessity, with confidence fusion proving most critical (12.1{\%} performance drop when removed). These advances enable more robust monitoring of evolving cryptocurrency threats while addressing fundamental challenges in knowledgeable foundation models."
}