Large Language Models for Cryptocurrency Transaction Analysis: A Bitcoin Case Study
This study tests how large language models (LLMs) can analyze complex Bitcoin transaction graphs for cybercrime detection. Researchers developed a three-tiered framework and two innovations—a human-readable format (LLM4TG) and a sampling algorithm (CETraS)—to handle data limits. Results show LLMs excel at understanding basic node details but struggle with global patterns, proving their potential for identifying suspicious transaction behavior.