  Large Language Models (LLMs) have made significant progress in utilizing
tools, but their ability is limited by API availability and the instability of
implicit reasoning, particularly when both planning and execution are involved.
To overcome these limitations, we propose CREATOR, a novel framework that
enables LLMs to create their own tools using documentation and code
realization. CREATOR disentangles abstract tool creation and concrete decision
execution, resulting in improved performance. We evaluate CREATOR on MATH and
TabMWP benchmarks, respectively consisting of challenging math competition
problems and diverse tabular contents. Remarkably, CREATOR outperforms existing
chain-of-thought, program-of-thought, and tool-using baselines. Additionally,
we introduce the Creation Challenge dataset, featuring 2K diverse questions, to
emphasize the necessity and benefits of LLMs' tool creation ability. Further
research demonstrates that leveraging LLMs as tool creators facilitates
knowledge transfer, and LLMs exhibit varying levels of tool creation abilities,
enabling them to adapt to diverse situations. The tool creation ability
revolutionizes the LLM's problem-solving paradigm, driving us closer to the
next frontier of artificial intelligence. All the codes and data are released.
