  This paper aims to efficiently enable Large Language Models (LLMs) to use
multimodal tools. Advanced proprietary LLMs, such as ChatGPT and GPT-4, have
shown great potential for tool usage through sophisticated prompt engineering.
Nevertheless, these models typically rely on prohibitive computational costs
and publicly inaccessible data. To address these challenges, we propose the
GPT4Tools based on self-instruct to enable open-source LLMs, such as LLaMA and
OPT, to use tools. It generates an instruction-following dataset by prompting
an advanced teacher with various multi-modal contexts. By using the Low-Rank
Adaptation (LoRA) optimization, our approach facilitates the open-source LLMs
to solve a range of visual problems, including visual comprehension and image
generation. Moreover, we provide a benchmark to evaluate the ability of LLMs to
use tools, which is performed in both zero-shot and fine-tuning ways. Extensive
experiments demonstrate the effectiveness of our method on various language
models, which not only significantly improves the accuracy of invoking seen
tools, but also enables the zero-shot capacity for unseen tools. The code and
demo are available at https://github.com/StevenGrove/GPT4Tools.
