LLM-TAD: Interpretable Ethereum Fraud Detection Based on Large Language Models

Abstract

As fraud patterns in the Ethereum ecosystem become increasingly sophisticated, traditional detection methods face limited generalization capability and insufficient interpretability. Although Large Language Models (LLMs) possess powerful semantic understanding and reasoning abilities, their direct application in fraud detection still suffers from critical issues, including inadequate domain knowledge integration and scarcity of high-quality interpretable training data. To address these challenges, this paper proposes Large Language Model for Transaction Anomaly Detection (LLM-TAD), a framework that constructs interpretable training data through a dual interpretation strategy combining XGBoost with SHAP/LIME to provide complementary feature-level insights, and achieves domain knowledge injection and capability optimization via a two-stage approach involving supervised fine-tuning and instruction fine-tuning. Experimental results demonstrate that the proposed method achieves a fraud detection accuracy of 93.01% and an explanation quality (BERTScore) of 0.7939, achieving synergistic improvement in both accuracy and interpretability.