Look Before You Leap: Towards Decision-Aware and Generalizable Tool-Usage for Large Language Models
<|reference_start|>Look Before You Leap: Towards Decision-Aware and Generalizable Tool-Usage for Large Language Models: 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.<|reference_end|>
@article{gui2024look,
    title={Look Before You Leap: Towards Decision-Aware and Generalizable
  Tool-Usage for Large Language Models},
    author={Anchun Gui, Jian Li, Yong Dai, Nan Du, Han Xiao},
    journal={arXiv preprint arXiv:2402.16696},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16696},
    primaryClass={cs.CL}
}