problems, Flan-PaLM with CoT + SC improves SOTA signiﬁcantly, achieving high performance even on
under-represented languages, such as 69.6% on Bengali. In comparison, PaLM with CoT + SC only achieves
63.6% with French and 61.2% on German, which are high-resource languages. As a ﬁnal result, on GSM8K
(Cobbe et al., 2021, not shown in the table), Flan-PaLM with CoT + SC achieves a new state of the art of
83.9%, though note that the GSM8K training dataset is included in the instruction ﬁnetuning mixture.
Scaling Instruction-Finetuned Language Models