Summary of Bench-coe: a Framework For Collaboration Of Experts From Benchmark, by Yuanshuai Wang et al.
Bench-CoE: a Framework for Collaboration of Experts from Benchmark
by Yuanshuai Wang, Xingjian Zhang, Jinkun Zhao, Siwei Wen, Peilin Feng, Shuhao Liao, Lei Huang, Wenjun Wu
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 the Bench-CoE framework, a system that enables Collaboration of Experts (CoE) by leveraging benchmark evaluations to achieve optimal performance across various tasks. The framework includes expert models, a router for task assignment, and a benchmark dataset for training. Two approaches are formulated: Query-Level and Subject-Level, which analyze merits and drawbacks. Experiments on language and multimodal tasks validate that Bench-CoE outperforms single models in overall performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers work better by letting different experts team up to do tasks. It creates a system called Bench-CoE that uses benchmark tests to figure out the best way for experts to work together. The system has three parts: expert models, a task-assigning router, and training data. The authors also try two ways of using this system and test it on different types of tasks. |
