Skills-in-Context Prompting: Unlocking Compositionality in Large
  Language Models
Authors: name: Jiaao Chen, name: Xiaoman Pan, name: Dian Yu, name: Kaiqiang Song, name: Xiaoyang Wang, name: Dong Yu, name: Jianshu Chen

We consider the problem of eliciting compositional generalization
capabilities in large language models (LLMs) with a novel type of prompting
strategy. Compositional generalization empowers the LLMs to solve problems that
are harder than the ones they have seen (i.e., easy-to-hard generalization),
which is a critical reasoning capability of human-like intelligence. However,
even the current state-of-the-art LLMs still struggle with this form of
reasoning. To bridge this gap, we propose skills-in-context (SKiC) prompting,
which instructs LLMs how to compose basic skills to resolve more complex
problems. We find that it is crucial to demonstrate both the skills and the
compositional examples within the same prompting context. With as few as two
examplars, our SKiC prompting initiates strong synergies between skills and
their composition capabilities. Notably, it empowers LLMs to solve unseen
problems that require innovative skill compositions, achieving near-perfect
generalization on a broad range of challenging compositionality tasks.
Intriguingly, SKiC prompting unlocks the latent potential of LLMs, enabling
them to leverage pre-existing internal skills acquired during earlier
pre-training stages, even when these skills are not explicitly presented in the
prompting context. This results in the capability of LLMs to solve unseen
complex problems by activating and composing internal competencies. With such
prominent features, SKiC prompting is able to achieve state-of-the-art
performance on challenging mathematical reasoning benchmarks (e.g., MATH).