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Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario
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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.
Forward citations
Cited by 2 Pith papers
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MassTool: A Multi-Task Search-Based Tool Retrieval Framework for Large Language Models
A multi-task retriever that combines tool-usage detection with query-centered graph and search-based modules improves tool retrieval accuracy over prior baselines.
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Unsupervised Query Routing for Retrieval Augmented Generation
An unsupervised method labels queries by comparing each search engine's answer to a multi-engine 'upper-bound' answer, then trains a router on those labels.
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