Summary of Do Large Language Models Need a Content Delivery Network?, by Yihua Cheng et al.
Do Large Language Models Need a Content Delivery Network?
by Yihua Cheng, Kuntai Du, Jiayi Yao, Junchen Jiang
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper explores the importance of efficiently injecting new knowledge into large language models (LLMs) to support their increasing range of applications. The authors argue that fine-tuning and in-context learning are popular methods, but using key-value caches as a medium for knowledge injection could offer more modular management and efficient LLM serving with low cost and fast response. To achieve this, they propose the Knowledge Delivery Network (KDN), a new system component that optimizes storage, transfer, and composition of KV caches across LLM engines and other resources. The authors believe that KDNs will be crucial to the success of LLM applications just as content delivery networks enabled the internet ecosystem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how we can make large language models smarter by adding new information to them. Right now, there are three ways to do this: fine-tune the model, add the information as part of what you’re asking it, or inject the knowledge directly into the model. The authors think that injecting the knowledge is the best way because it makes it easier to manage and use the new information. They propose a new system called the Knowledge Delivery Network (KDN) that can help make this process more efficient. |
Keywords
» Artificial intelligence » Fine tuning
