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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.16809 |
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| _version_ | 1866910714174636032 |
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| author | Gong, Jianghao Yan, Peiqi Zhang, Yue An, Hongli Liu, Logan |
| author_facet | Gong, Jianghao Yan, Peiqi Zhang, Yue An, Hongli Liu, Logan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16809 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Blockchain Meets LLMs: A Living Survey on Bidirectional Integration Gong, Jianghao Yan, Peiqi Zhang, Yue An, Hongli Liu, Logan Cryptography and Security Artificial Intelligence 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. |
| title | Blockchain Meets LLMs: A Living Survey on Bidirectional Integration |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2411.16809 |