We observe that PaLM 2 signiﬁcantly outperforms PaLM in the standard prompting setting across all languages, and
achieves particularly strong improvements on under-represented languages such as Swahili, Quechua, and Haitian.
Furthermore, PaLM 2 in the standard prompting setting outperforms PaLM using chain-of-thought prompting, demon-
strating strong multilingual reasoning capabilities of the underlying model out of the box.

A.4 Coding

Raw pass@1 results for BabelCode (Orlanski et al., 2023) are shown in Table 18.

11We employ a generative formulation of XCOPA following Shi et al. (2023) where the model is conditioned on the two choices and then has to

generate the correct one.

43

Table 18: Pass rates for PaLM and PaLM-2 experiments on BabelCode (Orlanski et al., 2023). All results are pass@1
and sampled greedily.

PaLM 2-S*

PaLM 540B PaLM-Coder-540B

language
C#
C++
Go
Haskell
Java
JS
Julia
Lua
PHP
Python
Rust
TS
PaLM 2 Technical Report