Liuchu Liuchu
2024
Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario
Feiteng Mu
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Yong Jiang
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Liwen Zhang
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Liuchu Liuchu
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Wenjie Li
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Pengjun Xie
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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.
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Co-authors
- Feiteng Mu 1
- Yong Jiang 1
- Liwen Zhang 1
- Wenjie Li 1
- Pengjun Xie 1
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