Summary of Real-time Adapting Routing (rar): Improving Efficiency Through Continuous Learning in Software Powered by Layered Foundation Models, By Kirill Vasilevski et al.
Real-time Adapting Routing (RAR): Improving Efficiency Through Continuous Learning in Software Powered by Layered Foundation Models
by Kirill Vasilevski, Dayi Lin, Ahmed Hassan
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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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 proposes Real-time Adaptive Routing (RAR), an approach to dynamically adapt routing decisions for Foundation Models (FMs) in software systems. Existing routing models rely on curated data and complex computations, neglecting the potential evolution of weaker FMs. RAR continuously updates FM routing decisions while leveraging guided in-context learning to enhance weaker FMs’ capabilities. This aims to reduce reliance on stronger, more expensive FMs. The approach is evaluated on subsets of the MMLU benchmark, showing a 50.2% reduction in requests routed to computationally expensive models while maintaining 90.5% response quality. Additionally, guides generated from stronger models demonstrate intra-domain generalization and improve response quality compared to standalone weaker FMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to make software systems use different large language models (LLMs) more efficiently. Right now, people often train a special model to decide which LLM to use for each task. This can be slow and doesn’t take into account when weaker LLMs might get better over time. The new approach, called Real-time Adaptive Routing, learns how to make these decisions in real-time while also helping weaker LLMs get better. This way, the system can rely less on stronger, more expensive LLMs. The researchers tested this approach and found that it works well, reducing the need for strong LLMs by 50% while keeping response quality high. |
Keywords
» Artificial intelligence » Domain generalization
