用大模型提升区块链数据分析能力，解决传统工具的局限性。
Blockchain Data Analysis in the Era of Large-Language Models
- 引入大语言模型增强区块链数据的推理与理解能力
- 系统梳理大模型在链上分析中的技术路径与设计模式
- 适合研究者、开发者及政策制定者参考前沿方向
区块链数据分析对挖掘洞察、追踪交易、识别模式以及保障去中心化网络的完整性与安全性至关重要。其应用涵盖欺诈检测、合规监管、智能合约审计和去中心化金融（DeFi）风险管控等多个领域。然而，现有分析工具面临数据稀缺、泛化能力不足及缺乏推理能力等挑战。我们认为大语言模型（LLMs）可缓解这些问题，但目前尚无系统性探讨其在区块链分析中集成的研究。本文系统性地探索了大模型融合于区块链数据分析的潜在技术与设计范式，并提出未来研究机遇与挑战，强调该领域亟需深入探索。本论文旨在为学术界、产业界及政策制定者提供有价值的方向指引。
原文摘要 · Abstract (English)
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.
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