Tasks. We use the following two toy tasks.
• Last letter concatenation. This task asks the model
to concatenate the last letters of words in a name (e.g.,
“Amy Brown” → “yn”). It is a more challenging version
of ﬁrst letter concatenation, which language models can
already perform without chain of thought.3 We generate
full names by randomly concatenating names from the
top one-thousand ﬁrst and last names from name census
data (https://namecensus.com/).

• Coin ﬂip. This task asks the model to answer whether a
coin is still heads up after people either ﬂip or don’t ﬂip
the coin (e.g., “A coin is heads up. Phoebe ﬂips the coin.
Osvaldo does not ﬂip the coin. Is the coin still heads up?”
→ “no”).

100

)

%

(

e
t
a
r

e
v
l
o
S

75

50

25

0

100

)

%

(

e
t
a
r

e
v
l
o
S

80

60

40

8

62

540

8

62

540

Model scale (# parameters in billions)

Figure 8:
Using chain-of-thought
prompting facilitates generalization to
longer sequences in two symbolic rea-
soning tasks.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models