EMNLP 2024finding2 citations
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
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.
BibTeX
@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/",
doi = "10.18653/v1/2024.findings-emnlp.598",
pages = "10225--10230"
}