Summary of Routoo: Learning to Route to Large Language Models Effectively, by Alireza Mohammadshahi et al.
Routoo: Learning to Route to Large Language Models Effectively
by Alireza Mohammadshahi, Arshad Rafiq Shaikh, Majid Yazdani
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Routoo architecture optimizes the selection of Large Language Models (LLMs) for specific prompts based on performance, cost, and efficiency. This approach allows for controllability over the trade-off between inference cost and quality, enabling significant reductions in inference costs for a given quality requirement. The architecture consists of two key components: a performance predictor that estimates the expected performance of various underlying LLMs without executing them, and a cost-aware selector module that selects the most suitable model based on these predictions and constraints such as cost and latency. Experimental results demonstrate that Routoo matches the performance of Mixtral 8x7b while reducing inference costs by one-third, and even surpasses its accuracy by over 5% at equivalent costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Routoo is a new way to use Large Language Models (LLMs) that makes them more efficient. Right now, some LLMs are really good but also very expensive to use. This can make it hard for people to use them in certain situations. Routoo helps by choosing the right model for the job, based on how well it will do and how much it will cost. It’s like a shortcut that lets you get the same quality without spending as much money. |
Keywords
* Artificial intelligence * Inference
