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The Role of Transformer Models in Advancing Blockchain Technology: A Systematic Survey

Tianxu Liu, Yanbin Wang, Jianguo Sun, Ye Tian, Yanyu Huang, Tao Xue, Peiyue Li, Yiwei Liu

TL;DR

This paper surveys how Transformer models are applied to blockchain to tackle efficiency, security, and scalability challenges. It presents a domain-oriented classification and reviews over 200 papers, detailing practical deployments in anomaly detection, smart contract security analysis, cryptocurrency prediction, and code summarization. The article discusses Transformer architectures, their derivatives, and how they handle blockchain data, plus the challenges of data privacy, model complexity, and real-time processing. It also outlines future directions to tailor Transformer designs to blockchain use cases and foster AI-blockchain integration.

Abstract

As blockchain technology rapidly evolves, the demand for enhanced efficiency, security, and scalability grows.Transformer models, as powerful deep learning architectures,have shown unprecedented potential in addressing various blockchain challenges. However, a systematic review of Transformer applications in blockchain is lacking. This paper aims to fill this research gap by surveying over 200 relevant papers, comprehensively reviewing practical cases and research progress of Transformers in blockchain applications. Our survey covers key areas including anomaly detection, smart contract security analysis, cryptocurrency prediction and trend analysis, and code summary generation. To clearly articulate the advancements of Transformers across various blockchain domains, we adopt a domain-oriented classification system, organizing and introducing representative methods based on major challenges in current blockchain research. For each research domain,we first introduce its background and objectives, then review previous representative methods and analyze their limitations,and finally introduce the advancements brought by Transformer models. Furthermore, we explore the challenges of utilizing Transformer, such as data privacy, model complexity, and real-time processing requirements. Finally, this article proposes future research directions, emphasizing the importance of exploring the Transformer architecture in depth to adapt it to specific blockchain applications, and discusses its potential role in promoting the development of blockchain technology. This review aims to provide new perspectives and a research foundation for the integrated development of blockchain technology and machine learning, supporting further innovation and application expansion of blockchain technology.

The Role of Transformer Models in Advancing Blockchain Technology: A Systematic Survey

TL;DR

This paper surveys how Transformer models are applied to blockchain to tackle efficiency, security, and scalability challenges. It presents a domain-oriented classification and reviews over 200 papers, detailing practical deployments in anomaly detection, smart contract security analysis, cryptocurrency prediction, and code summarization. The article discusses Transformer architectures, their derivatives, and how they handle blockchain data, plus the challenges of data privacy, model complexity, and real-time processing. It also outlines future directions to tailor Transformer designs to blockchain use cases and foster AI-blockchain integration.

Abstract

As blockchain technology rapidly evolves, the demand for enhanced efficiency, security, and scalability grows.Transformer models, as powerful deep learning architectures,have shown unprecedented potential in addressing various blockchain challenges. However, a systematic review of Transformer applications in blockchain is lacking. This paper aims to fill this research gap by surveying over 200 relevant papers, comprehensively reviewing practical cases and research progress of Transformers in blockchain applications. Our survey covers key areas including anomaly detection, smart contract security analysis, cryptocurrency prediction and trend analysis, and code summary generation. To clearly articulate the advancements of Transformers across various blockchain domains, we adopt a domain-oriented classification system, organizing and introducing representative methods based on major challenges in current blockchain research. For each research domain,we first introduce its background and objectives, then review previous representative methods and analyze their limitations,and finally introduce the advancements brought by Transformer models. Furthermore, we explore the challenges of utilizing Transformer, such as data privacy, model complexity, and real-time processing requirements. Finally, this article proposes future research directions, emphasizing the importance of exploring the Transformer architecture in depth to adapt it to specific blockchain applications, and discusses its potential role in promoting the development of blockchain technology. This review aims to provide new perspectives and a research foundation for the integrated development of blockchain technology and machine learning, supporting further innovation and application expansion of blockchain technology.
Paper Structure (37 sections, 20 figures, 5 tables)

This paper contains 37 sections, 20 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Overview of the Transformer Model and Its Derivatives
  3. Core Components of the Transformer Model
  4. Self-Attention Mechanism
  5. Multi-Head Attention Mechanism
  6. Detailed Architecture of the Transformer
  7. Encoder
  8. Decoder
  9. Derivative Models Based on the Transformer
  10. BERT(Bidirectional Encoder Representations from Transformers)
  11. GPT(Generative Pre-trained Transformer)
  12. RoBERTa(Robustly Optimized BERT approach)
  13. T5(Text-to-Text Transfer Transformer)
  14. Fundamentals of Blockchain Technology
  15. Overview of Blockchain Technology
  16. ...and 22 more sections

Figures (20)

  • Figure 1: Statistics on the number of research papers on transformer models.
  • Figure 2: Statistics on the number of research papers on the application of transformer models to blockchain.
  • Figure 3: A diverse set of application areas of Transformers in blockchain covered in this survey.
  • Figure 4: Multi head attention mechanism. In the encoder and decoder, multiple attention heads are stacked together and their outputs are concatenated
  • Figure 5: Architecture of the Transformer Model.
  • ...and 15 more figures