words, our work underscores that standard prompting only provides a lower bound on the capabilities
of large language models. This observation likely raises more questions than it answers—for instance,
how much more can we expect reasoning ability to improve with a further increase in model scale?
What other prompting methods might expand the range of tasks that language models can solve?
As for limitations, we ﬁrst qualify that although chain of thought emulates the thought processes of
human reasoners, this does not answer whether the neural network is actually “reasoning,” which
we leave as an open question. Second, although the cost of manually augmenting exemplars with
chains of thought is minimal in the few-shot setting, such annotation costs could be prohibitive for
ﬁnetuning (though this could potentially be surmounted with synthetic data generation, or zero-shot
generalization). Third, there is no guarantee of correct reasoning paths, which can lead to both correct
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models