4.3 Unlocking zero-shot reasoning
A ﬁnal beneﬁt of instruction ﬁnetuning on CoT data both with and without exemplars is that the resulting
model is able to perform CoT reasoning in a zero-shot setting. This zero-shot setting is important because it
tests the ability for the model to produce its own reasoning skills without few-shot exemplars for CoT, which
can require substantial prompt engineering to compose properly.
Figure 6 shows that for the BBH benchmark of 23 unseen challenging BIG-Bench tasks, Flan-PaLM models can
achieve improved performance by leveraging CoT reasoning activated by the phrase “let’s think step-by-step”
(Kojima et al., 2022). In comparison, PaLM without ﬁnetuning does not generate CoT that allows it to solve
these problems. Three examples of zero-shot CoT are shown for PaLM and Flan-PaLM in Figure 7.
Although the negative zero-shot CoT result on PaLM may appear to contradict the ﬁndings from Kojima et al.
Scaling Instruction-Finetuned Language Models