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.

翻译：当前工具学习的研究主要集中于从众多选项中选择最有效的工具，往往忽视了成本效益这一人类问题解决中的关键因素。本文通过预测同质化工具的性能及完成特定任务所需的相关成本，来解决工具选择问题。我们以成本效益最优的方式将查询分配给最合适的工具。实验结果表明，与现有强基线方法相比，我们的方法能够以更低的成本实现更高的性能。