Recent advancements in integrating large language models (LLMs) with tools
have allowed the models to interact with real-world environments. However,
these tool-augmented LLMs often encounter incomplete scenarios when users
provide partial information or the necessary tools are unavailable. Recognizing
and managing such scenarios is crucial for LLMs to ensure their reliability,
but this exploration remains understudied. This study examines whether LLMs can
identify incomplete conditions and appropriately determine when to refrain from
using tools. To this end, we address a dataset by manipulating instances from
two datasets by removing necessary tools or essential information for tool
invocation. We confirm that most LLMs are challenged to identify the additional
information required to utilize specific tools and the absence of appropriate
tools. Our research can contribute to advancing reliable LLMs by addressing
scenarios that commonly arise during interactions between humans and LLMs.

本研究探讨了大型语言模型与工具的整合，以及在用户提供不完整信息或无法使用所需工具时如何识别并管理这些不完整场景，从而提高语言模型的可靠性，并验证了大多数语言模型在识别特定工具所需的附加信息和适当工具的缺失方面存在困难。