We focus on the 1-shot-learning setting, as inputs can be long. We truncate extremely long inputs to about half the max
input length, so that instructions and targets can always ﬁt within the model’s input. We decode a single output greedily
and stop at an exemplar separator (double newline), or continue decoding until the maximum decode length, which is
set to the 99th-percentile target length.

10We focus on the set of typologically diverse languages also used in TyDi QA (Clark et al., 2020).

19

Table 11: One-shot NLG evaluation results. We report ROUGE-2 for English and SentencePiece-ROUGE-2 for all other
languages.

Task
XSum
WikiLingua

XLSum

Languages
en
ar, ja, ko, ru,
th, tr
ar, bn, en, ja,
in, sw, ko, ru,
te, th, tr

PaLM PaLM 2-S
14.5
11.7

16.9
16.8

12.7

18.3

PaLM 2-M PaLM 2-L

17.2
17.6

19.1

23.2
23.5

21.3
PaLM 2 Technical Report