Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario
<|reference_start|>Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario: 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.<|reference_end|>
@article{mu2024query,
    title={Query Routing for Homogeneous Tools: An Instantiation in the RAG
  Scenario},
    author={Feiteng Mu, Yong Jiang, Liwen Zhang, Chu Liu, Wenjie Li, Pengjun Xie,
  Fei Huang},
    journal={arXiv preprint arXiv:2406.12429},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12429},
    primaryClass={cs.AI}
}