Summary of Hybrid Llm: Cost-efficient and Quality-aware Query Routing, by Dujian Ding et al.
Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing
by Dujian Ding, Ankur Mallick, Chi Wang, Robert Sim, Subhabrata Mukherjee, Victor Ruhle, Laks V.S. Lakshmanan, Ahmed Hassan Awadallah
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 hybrid inference approach combines the strengths of large language models (LLMs) and smaller models deployed on edge devices to balance cost and quality. By routing queries based on predicted difficulty and desired quality levels, this method can reduce calls to LLMs by up to 40% while maintaining response quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to use language models that are big enough to work well but also small enough to run quickly on devices like smartphones. This helps save money because it doesn’t need expensive computers in the cloud. The system decides which type of model to use based on how hard a question is and how important it is to get a good answer. This makes it easier to balance between getting accurate answers and saving time and money. |
Keywords
» Artificial intelligence » Inference
