| Autor: |
Mu, Feiteng, Jiang, Yong, Zhang, Liwen, Liu, Chu, Li, Wenjie, Xie, Pengjun, Huang, Fei |
| Rok vydání: |
2024 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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