Recently, the astonishing performance of large language models (LLMs) in natural language comprehension and generation tasks triggered lots of exploration of using them as central controllers to build agent systems. Multiple studies focus on bridging the LLMs to external tools to extend the application scenarios. However, the current LLMs' perceiving tool-use ability is limited to a single text query, which may result in ambiguity in understanding the users' real intentions. LLMs are expected to eliminate that by perceiving the visual- or auditory-grounded instructions' information. Therefore, in this paper, we propose MLLM-Tool, a system incorporating open-source LLMs and multi-modal encoders so that the learnt LLMs can be conscious of multi-modal input instruction and then select the function-matched tool correctly. To facilitate the evaluation of the model's capability, we collect a dataset featured by consisting of multi-modal input tools from HuggingFace. Another important feature of our dataset is that our dataset also contains multiple potential choices for the same instruction due to the existence of identical functions and synonymous functions, which provides more potential solutions for the same query. The experiments reveal that our MLLM-Tool is capable of recommending appropriate tools for multi-modal instructions. Codes and data are available at https://github.com/MLLM-Tool/MLLM-Tool.

翻译：近期，大语言模型在自然语言理解与生成任务中展现出的惊人性能，激发了将其作为中央控制器构建智能体系统的广泛探索。诸多研究聚焦于连接大语言模型与外部工具以扩展应用场景。然而，当前大语言模型感知工具使用的能力局限于单一文本查询，这可能导致理解用户真实意图时产生歧义。大语言模型需通过感知基于视觉或听觉的指令信息来消除这种歧义。为此，本文提出MLLM-Tool系统，该系统融合开源大语言模型与多模态编码器，使训练后的大语言模型能够感知多模态输入指令，并正确选择功能匹配的工具。为便于评估模型能力，我们构建了一个以HuggingFace多模态输入工具为特色的数据集。该数据集的另一重要特征在于：由于同义函数及功能相同函数的存在，同一指令包含多个潜在候选工具，为同一查询提供了更多可行解决方案。实验表明，我们的MLLM-Tool能够为多模态指令推荐合适的工具。代码与数据已开源至https://github.com/MLLM-Tool/MLLM-Tool。