Large Language Models (LLMs) have demonstrated significant progress in
utilizing external APIs as tools for various tasks. However, their tool-using
ability is limited by the availability of suitable APIs and the instability of
implicit reasoning, particularly when simultaneously engaging in reasoning
about plans and actual calculations. To address these limitations, we propose
CREATOR, a novel framework that empowers LLMs to create their own tools through
documentation and code realization. CREATOR disentangles the LLM's ability into
two distinct phases: abstract tool creation and concrete decision execution,
which results in improved LLM performance. We evaluate CREATOR on two
established benchmarks: MATH, which consists of challenging math competition
problems, and TabMWP, which includes diverse tabular contents for
problem-solving. Remarkably, CREATOR significantly outperforms existing
chain-of-thought (CoT), program-of-thought (PoT), and tool-using baselines on
these two benchmarks. Additionally, we present a new dataset, Creation
Challenge, comprising 2K diverse questions, to highlight the necessity and
benefits of LLMs' tool creation ability in effectively addressing these
problems. Furthermore, our research reveals that leveraging LLMs as tool
creators facilitates knowledge transfer, and LLMs exhibit varying levels of
tool creation abilities, enabling them to flexibly tackle diverse situations.
Our study represents a promising avenue for maximizing the potential of LLMs
and advancing toward truly intelligent and adaptable AI systems.

CREATOR 是一种新颖的框架，使大型语言模型创建自己的工具变得容易，该方法在数学问题求解方面的表现显著优于现有方法，并有助于知识转移和提高模型的适应性。