Summary of Mess+: Energy-optimal Inferencing in Language Model Zoos with Service Level Guarantees, by Ryan Zhang et al.
MESS+: Energy-Optimal Inferencing in Language Model Zoos with Service Level Guarantees
by Ryan Zhang, Herbert Woisetschläger, Shiqiang Wang, Hans Arno Jacobsen
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 presents MESS+, an online stochastic optimization algorithm for selecting large language models (LLMs) from a model zoo, prioritizing energy efficiency and accuracy. The authors highlight the limitations of current model selection methods, which often rely on public benchmark leaderboards and educated guesses. Instead, they propose a per-inference-request approach that balances cost efficiency with output quality. MESS+ is shown to be up to 2.5x more energy efficient than randomly selecting an LLM from the zoo while maintaining high accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find the right language model for our tasks quickly and efficiently. It’s like a big library of models, but instead of browsing shelves, we can ask a special algorithm to pick the best one for each task. This algorithm is called MESS+, and it makes sure that the chosen model is both good and cost-effective. The authors show that MESS+ can be up to 2.5 times more energy-efficient than just picking a random model from the library, while still getting great results. |
Keywords
* Artificial intelligence * Inference * Language model * Optimization
