大语言模型时代的区块链数据分析
Blockchain Data Analysis in the Era of Large-Language Models
AI总结：
针对现有区块链数据分析工具存在的数据稀缺、泛化性差、缺推理能力等问题，本文系统探索大语言模型与区块链数据分析融合的技术、设计模式及研究机遇挑战，为相关领域受众提供洞见。
摘要：
区块链数据分析对于挖掘洞见、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，在欺诈检测、监管合规、智能合约审计、去中心化金融（DeFi）风险管理等多个领域发挥着关键作用。然而，现有区块链数据分析工具面临数据稀缺、泛化性不足、缺乏推理能力等挑战。我们认为大语言模型（LLMs）能够缓解这些挑战，但目前尚未有研究全面系统地探讨LLM与区块链数据分析的融合。本文系统探索了LLM赋能的区块链数据分析中的潜在技术与设计模式，还梳理了未来的研究机遇与挑战，强调这一前景广阔的领域有待进一步探索。本文旨在为学术界、产业界、政策制定领域的广泛受众提供参考，为LLM与区块链数据分析的融合提供有价值的洞见。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
