Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario
This addresses cost-effectiveness in tool selection for AI systems, but it is incremental as it focuses on homogeneous tools rather than broader tool arrays.
The paper tackles the problem of selecting homogeneous tools in tool learning by predicting both performance and cost to assign queries cost-effectively, achieving higher performance at lower cost compared to strong baselines.
Current research on tool learning primarily focuses on selecting the most effective tool from a wide array of options, often overlooking cost-effectiveness, a crucial factor in human problem-solving. In this paper, we address the selection of homogeneous tools by predicting both their performance and the associated cost required to accomplish a given task. We then assign queries to the optimal tools in a cost-effective manner. Our experimental results demonstrate that our method achieves higher performance at a lower cost compared to strong baseline approaches.