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 Tool-LMM, 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 LMM is capable of recommending appropriate tools for multi-modal instructions. Codes and data are available at https://github.com/Tool-LMM/Tool-LMM.

通过使用多模态编码器将开源大语言模型（LLM）与多模态输入指令结合起来，我们提出了Tool-LMM系统，使学习的LLMs能够意识到多模态输入指令并正确选择匹配功能的工具，实验证明我们的LMM能够为多模态指令推荐适当的工具。

Tool-LMM：一个用于工具智能学习的大型多模态模型