Summary of Composition Of Experts: a Modular Compound Ai System Leveraging Large Language Models, by Swayambhoo Jain et al.
Composition of Experts: A Modular Compound AI System Leveraging Large Language Models
by Swayambhoo Jain, Ravi Raju, Bo Li, Zoltan Csaki, Jonathan Li, Kaizhao Liang, Guoyao Feng, Urmish Thakkar, Anand Sampat, Raghu Prabhakar, Sumati Jairath
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
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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 Composition of Experts (CoE) is a modular AI system that leverages multiple expert Large Language Models (LLMs). CoE uses a router to dynamically select the most appropriate expert for a given input, enabling efficient resource utilization and improved performance. The paper formulates the general problem of training a CoE and discusses its inherent complexities. A two-step routing approach is proposed to address these complexities, involving classification of inputs into distinct categories followed by a category-to-expert mapping. CoE offers a flexible and cost-effective solution for building compound AI systems. Empirical evaluation demonstrates superior performance with reduced computational overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoE is a new way to make AI better. It uses lots of small expert AI models instead of one big model. This makes it cheaper, faster, and more accurate. The paper explains how CoE works and why it’s useful. It also shows that CoE can do things like language translation and text summarization really well. |
Keywords
» Artificial intelligence » Classification » Summarization » Translation
