  Scaling up language models has been empirically shown to improve performance
on a wide range of downstream tasks. However, if we were to observe worse
performance as a function of scale ("inverse scaling") on certain tasks, this
would indicate that scaling can also encourage behaviors that are misaligned
with human preferences. The Inverse Scaling Prize (McKenzie et al. 2022)
identified eleven such inverse scaling tasks, evaluated on models of up to 280B
parameters and up to 500 zettaFLOPs of training compute. This paper takes a
closer look at these inverse scaling tasks. We evaluate models of up to 540B
parameters, trained on five times more compute than those evaluated in the
Inverse Scaling Prize. With this increased range of model sizes and training
compute, only four out of the eleven tasks remain inverse scaling. Six out of
the eleven tasks exhibit "U-shaped scaling", where performance decreases up to
a certain size, and then increases again up to the largest model evaluated (the
one remaining task displays positive scaling). In addition, we find that 1-shot
examples and chain-of-thought can help mitigate undesirable scaling patterns
even further. U-shaped scaling suggests that the inverse scaling trend observed
in McKenzie et al. (2022) may not continue to hold for larger models, which we
attribute to the presence of distractor tasks that only sufficiently large
models can avoid.
