Blockchain Data Analysis in the Era of Large-Language Models
Kentaroh Toyoda, Xiao Wang, Mingzhe Li, Bo Gao, Yuan Wang, Qingsong Wei
TL;DR
Blockchain data analysis is essential yet hindered by data scarcity, cross-chain fragmentation, and limited explainability. The paper surveys and tutorials LLM-based integration, emphasizing prompt engineering, retrieval-augmented generation, and design patterns mapped to fraud detection, smart contract auditing, market analysis, governance monitoring, and privacy. It contributions a comprehensive integration framework, a taxonomy of prompt and design patterns, and a forward-looking research agenda addressing latency, reliability, cost, scalability, generalizability, and autonomy. The work provides practical guidance for academia, industry, and policy-makers to deploy explainable, cross-chain, LLM-powered blockchain analytics.
Abstract
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.