using natural language prompts evaluated over a set of k-
shot settings. We calculate the relevant term frequencies for
all model checkpoints based on the sampled pretraining data
that each checkpoint sees, which means counting through
each subset of the pretraining corpus sampled and seen by
the model up to each checkpoint. Model evaluation was
performed on the Pythia (Deduplicated) suite using the LM
Evaluation Harness (Gao et al., 2021).
Following Razeghi et al. (2022), the formulation of the
arithmetic task consists of input operands x1 ∈ [0, 99] and
x2 ∈ [1, 50] and an output y. The input operands are con-
verted into a prompt with the prompt template “Q:What is
x1 # x2? A:” with # being “plus” for addition and “times”
for multiplication. We measure the accuracy of a prompt
instance by checking the model’s prediction against y. To
measure the term frequency and task performance correla-
tion, the average accuracy of all prompts with the same x1
Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling