Summary of A Contemporary Overview: Trends and Applications Of Large Language Models on Mobile Devices, by Lianjun Liu et al.
A Contemporary Overview: Trends and Applications of Large Language Models on Mobile Devices
by Lianjun Liu, Hongli An, Pengxuan Chen, Longxiang Ye
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) are revolutionizing user experiences on mobile devices, enabling more natural and personalized interactions. With advancements in hardware technologies and network infrastructure, LLMs can be deployed locally on mobile devices for efficient inference and low-latency responses. This reduces reliance on cloud computing while improving data privacy and security. Open APIs and SDKs allow developers to integrate LLM functionalities, creating innovative intelligent applications. The widespread adoption of LLMs will enhance the intelligence of mobile devices and drive innovation in fields like augmented reality (AR) and the Internet of Things (IoT). This trend is expected to shape the next generation of mobile intelligent applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a super-smart phone that can understand you better than ever before. That’s what large language models are all about! They’re like really smart computers that can talk and learn from us. With these models, your phone can get smarter and more helpful, without needing to send all your data to the cloud. This is great for privacy and security. Developers can use special tools to make new apps that are even more intelligent. As more people use these language models, we’ll see even cooler things happen in areas like virtual reality and connected devices. |
Keywords
» Artificial intelligence » Inference
