In the domain of large language models, considerable advancements have been attained in multimodal large language models and explainability research, propelled by the continuous technological progress and innovation. Nonetheless, security and privacy concerns continue to pose as prominent challenges in this field. The emergence of blockchain technology, marked by its decentralized nature, tamper-proof attributes, distributed storage functionality, and traceability, has provided novel approaches for resolving these issues. Both of these technologies independently hold vast potential for development; yet, their combination uncovers substantial cross-disciplinary opportunities and growth prospects. The current research tendencies are increasingly concentrating on the integration of blockchain with large language models, with the aim of compensating for their respective limitations through this fusion and promoting further technological evolution. In this study, we evaluate the advantages and developmental constraints of the two technologies, and explore the possibility and development potential of their combination. This paper primarily investigates the technical convergence in two directions: Firstly, the application of large language models to blockchain, where we identify six major development directions and explore solutions to the shortcomings of blockchain technology and their application scenarios; Secondly, the application of blockchain technology to large language models, leveraging the characteristics of blockchain to remedy the deficiencies of large language models and exploring its application potential in multiple fields.

翻译：在大语言模型领域，随着技术的持续进步与创新，多模态大语言模型与可解释性研究已取得显著进展。然而，安全与隐私问题仍是该领域面临的突出挑战。区块链技术凭借其去中心化特性、防篡改属性、分布式存储功能及可追溯性，为解决这些问题提供了新途径。这两项技术各自具备巨大的发展潜力，而它们的结合则展现出显著的跨学科机遇与增长前景。当前研究趋势日益聚焦于区块链与大语言模型的融合，旨在通过技术互补克服各自局限，推动技术的进一步发展。本研究评估了两种技术的优势与发展限制，并探讨了二者结合的可能性与发展潜力。本文主要从两个方向考察技术融合：首先，探讨大语言模型在区块链领域的应用，我们识别出六大发展方向，并探索区块链技术缺陷的解决方案及其应用场景；其次，研究区块链技术在大语言模型中的应用，利用区块链特性弥补大语言模型的不足，并探索其在多个领域的应用潜力。