  The meaning of complex phrases in natural language is composed of their
individual components. The task of compositional generalization evaluates a
model's ability to understand new combinations of components. Previous studies
trained smaller, task-specific models, which exhibited poor generalization.
While large language models (LLMs) exhibit impressive generalization abilities
on many tasks through in-context learning (ICL), their potential for
compositional generalization remains unexplored. In this paper, we first
empirically investigate prevailing ICL methods in compositional generalization.
We find that they struggle with complex compositional questions due to
cumulative errors in long reasoning steps and intricate logic required for
tool-making. Consequently, we propose a human-guided tool manipulation
framework (HTM) that generates tools for sub-questions and integrates multiple
tools. Our method enhances the effectiveness of tool creation and usage with
minimal human effort. Experiments show that our method achieves
state-of-the-art performance on two compositional generalization benchmarks and
outperforms existing methods on the most challenging test split by 70%.
