Blockchain Data Analysis in the Era of Large-Language Models
It addresses the problem of improving blockchain analysis for fraud detection and compliance, but it is incremental as it reviews and proposes ideas without new empirical findings.
This paper tackles the challenges in blockchain data analysis, such as data scarcity and lack of reasoning, by exploring the integration of large language models (LLMs) to enhance capabilities, but it does not present specific results or numbers.
Blockchain data analysis is essential for deriving insights, tracking transactions, identifying patterns, and ensuring the integrity and security of decentralized networks. It plays a key role in various areas, such as fraud detection, regulatory compliance, smart contract auditing, and decentralized finance (DeFi) risk management. However, existing blockchain data analysis tools face challenges, including data scarcity, the lack of generalizability, and the lack of reasoning capability. We believe large language models (LLMs) can mitigate these challenges; however, we have not seen papers discussing LLM integration in blockchain data analysis in a comprehensive and systematic way. This paper systematically explores potential techniques and design patterns in LLM-integrated blockchain data analysis. We also outline prospective research opportunities and challenges, emphasizing the need for further exploration in this promising field. This paper aims to benefit a diverse audience spanning academia, industry, and policy-making, offering valuable insights into the integration of LLMs in blockchain data analysis.