Large language models have recently shown a remarkable ability for few-shot learning, including patterns of algorithmic nature. It is now time to ask what kind of patterns these models can capture and how many examples they need in their prompts. We frame this question as a teaching problem with strong priors, and study whether language models can identify simple algorithmic concepts from small witness sets. In particular, we explore how several GPT architectures, program induction and humans pe…