Liuchu Liuchu


2024

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Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario
Feiteng Mu | Yong Jiang | Liwen Zhang | Liuchu Liuchu | Wenjie Li | Pengjun Xie | Fei Huang
Findings of the Association for Computational Linguistics: EMNLP 2024

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 query routing for 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.