Fig. 6: a) Effectiveness of shaping specific personality traits in the model’s output. For each
of the traits defined in the IPIP-NEO personality domains, we plot the distribution of scores
when the model is prompted to shape the output traits to have low levels (blue) and when
the model is prompted to shape the output traits to have high levels (red). When prompting
a model to simulate a low level of a trait, the observed mean of the distribution of the trait’s
IPIP-NEO subscale is between 1.00 and 2.00, and when prompting a model to simulate a
high level of a trait it is between 4.00 & 5. Observing this clear difference in distributions
for low vs high traits in all five dimensions is desirable as it indicates the prompt effectively
influences the produced scores. b) Ability of Flan-PaLM 540B’s IPIP-NEO scores (in blue)
to accurately predict personality levels in a downstream task to generate social media status
PersonalityTraitsinLargeLanguageModels