Summary of Routellm: Learning to Route Llms with Preference Data, by Isaac Ong et al.
RouteLLM: Learning to Route LLMs with Preference Data
by Isaac Ong, Amjad Almahairi, Vincent Wu, Wei-Lin Chiang, Tianhao Wu, Joseph E. Gonzalez, M Waleed Kadous, Ion Stoica
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
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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 In this paper, researchers tackle the challenge of choosing between large language models (LLMs) that excel in various tasks but come with varying costs. The authors propose efficient router models that can dynamically select between stronger and weaker LLMs during inference to optimize cost-response quality trade-offs. They develop a training framework for these routers using human preference data and data augmentation techniques to boost performance. The evaluation on well-known benchmarks shows that their approach can significantly reduce costs (by over 2 times in some cases) without compromising response quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers designed special models called “routers” that can choose between two different language models, a stronger one and a weaker one. This helps balance the cost of getting an answer with how good the answer is. The team used data from humans to train these routers, which showed big improvements in performance. Even when they switched to new models for testing, the routers still worked well. |
Keywords
* Artificial intelligence * Data augmentation * Inference
