  Tool-augmented large language models (LLMs) are attracting widespread
attention when accessing up-to-date knowledge and alleviating hallucination
issues. Nowadays, advanced closed-source LLMs (e.g., ChatGPT) have demonstrated
surprising tool-usage capabilities through prompting and in-context learning
techniques. To empower the capabilities of open-source LLMs (e.g., LLaMA) in
manipulating tools, current efforts focus on either template-driven or
token-triggered tool-usage. However, the former hampers LLMs' flexibility to
address diverse user's queries due to constrained tool interactions, while the
latter limits the generalizability when engaging with new tools, since
tool-usage learning is based on task- and tool-specific datasets. To alleviate
these concerns, in this paper, we propose a decision-aware and generalizable
tool-usage framework (DEER). Specifically, we first construct the tool-usage
samples with multiple decision branches via an automatic generation pipeline,
thereby inspiring the decision-making awareness of LLMs under diverse
scenarios. Meanwhile, we propose a novel tool sampling strategy to enhance the
generalizability of LLMs over unseen tools. Extensive experiments demonstrate
that our proposed DEER is effective and significantly outperforms baselines
across various datasets.
