Knowledge-Grounded Detection of Cryptocurrency Scams with Retrieval-Augmented LMs

AbstractThis 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× 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.