Summary of Query Routing For Homogeneous Tools: An Instantiation in the Rag Scenario, by Feiteng Mu et al.
Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario
by Feiteng Mu, Yong Jiang, Liwen Zhang, Chu Liu, Wenjie Li, Pengjun Xie, Fei Huang
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 tackles tool learning, focusing on selecting the most effective and cost-effective tools for a specific task. The authors propose a method that predicts both the performance and associated cost of different tools, then assigns queries to the optimal tool in a cost-conscious manner. Experimental results show that this approach outperforms strong baselines while reducing costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn how to choose the best tools for a job. Right now, we mostly focus on picking the best tool from many options, but we forget about how much it will cost. The authors want to change this by predicting not only which tool is best but also how much it will cost. They then use this information to pick the right tool for the task at hand. The results show that their method is better and cheaper than other methods. |
