  Tool learning methods have enhanced the ability of large language models
(LLMs) to interact with real-world applications. Many existing works fine-tune
LLMs or design prompts to enable LLMs to select appropriate tools and correctly
invoke them to meet user requirements. However, it is observed in previous
works that the performance of tool learning varies from tasks, datasets,
training settings, and algorithms. Without understanding the impact of these
factors, it can lead to inconsistent results, inefficient model deployment, and
suboptimal tool utilization, ultimately hindering the practical integration and
scalability of LLMs in real-world scenarios. Therefore, in this paper, we
explore the impact of both internal and external factors on the performance of
tool learning frameworks. Through extensive experiments on two benchmark
datasets, we find several insightful conclusions for future work, including the
observation that LLMs can benefit significantly from increased trial and
exploration. We believe our empirical study provides a new perspective for
future tool learning research.
