Summary of Ccoe: a Compact and Efficient Llm Framework with Multi-expert Collaboration For Resource-limited Settings, by Shaomang Huang et al.
CCoE: A Compact and Efficient LLM Framework with Multi-Expert Collaboration for Resource-Limited Settings
by Shaomang Huang, Jianfeng Pan, Min Peng, Hanzhong Zheng
First submitted to arxiv on: 16 Jul 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 The proposed CCoE architecture is a modular framework that integrates domain-specific experts into a unified Large Language Model (LLM), achieving state-of-the-art performance with reduced resource requirements for multi-expert deployments. This approach leverages independently trained expert subnetworks on a shared backbone partition, enabling flexible task allocation and promoting expert collaboration to handle complex reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The CCoE architecture is designed to scale LLMs to support multiple downstream domain applications while reducing resource constraints. It achieves this by seamlessly integrating domain-specific experts into a unified LLM, which allows for efficient inference and reduces memory usage. The proposed approach also enables flexible task allocation, allowing experts to collaborate on complex reasoning tasks. |
Keywords
» Artificial intelligence » Inference » Large language model
