@inproceedings{mu-etal-2024-query,
title = "Query Routing for Homogeneous Tools: An Instantiation in the {RAG} Scenario",
author = "Mu, Feiteng and
Jiang, Yong and
Zhang, Liwen and
Liuchu, Liuchu and
Li, Wenjie and
Xie, Pengjun and
Huang, Fei",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.598",
pages = "10225--10230",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario
%A Mu, Feiteng
%A Jiang, Yong
%A Zhang, Liwen
%A Liuchu, Liuchu
%A Li, Wenjie
%A Xie, Pengjun
%A Huang, Fei
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F mu-etal-2024-query
%X 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.
%U https://aclanthology.org/2024.findings-emnlp.598
%P 10225-10230
Markdown (Informal)
[Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario](https://aclanthology.org/2024.findings-emnlp.598) (Mu et al., Findings 2024)
ACL
- Feiteng Mu, Yong Jiang, Liwen Zhang, Liuchu Liuchu, Wenjie Li, Pengjun Xie, and Fei Huang. 2024. Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 10225–10230, Miami, Florida, USA. Association for Computational Linguistics.