Blockchain Data Analysis in the Era of Large-Language Models

TL;DR

This paper explores how large language models can enhance blockchain data analysis by addressing current challenges, proposing techniques, design patterns, and outlining future research opportunities in this emerging field.

Contribution

It systematically investigates the integration of LLMs into blockchain data analysis, offering a comprehensive overview of techniques, design patterns, and future research directions.

Findings

Identifies key challenges in blockchain data analysis.

Proposes potential techniques for LLM integration.

Outlines future research opportunities and challenges.

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.