Abstract:With the rapid development of large language models (LLMs), which possess powerful natural language processing and generation capabilities, LLMs are poised to provide more natural and personalized user experiences. Their deployment on mobile devices is gradually becoming a significant trend in the field of intelligent devices. LLMs have demonstrated tremendous potential in applications such as voice assistants, real-time translation, and intelligent recommendations. Advancements in hardware technologies (such as neural network accelerators) and network infrastructure (such as 5G) have enabled efficient local inference and low-latency intelligent responses on mobile devices. This reduces reliance on cloud computing while enhancing data privacy and security. Developers can easily integrate LLM functionalities through open APIs and SDKs, enabling the creation of more innovative intelligent applications. The widespread use of LLMs not only enhances the intelligence of mobile devices but also fosters the integrated innovation of fields like augmented reality (AR) and the Internet of Things (IoT). This trend is expected to drive the development of the next generation of mobile intelligent applications.
Abstract: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.