  Psychology research has long explored aspects of human personality such as
extroversion, agreeableness and emotional stability. Categorizations like the
`Big Five' personality traits are commonly used to assess and diagnose
personality types. In this work, we explore the question of whether the
perceived personality in language models is exhibited consistently in their
language generation. For example, is a language model such as GPT2 likely to
respond in a consistent way if asked to go out to a party? We also investigate
whether such personality traits can be controlled. We show that when provided
different types of contexts (such as personality descriptions, or answers to
diagnostic questions about personality traits), language models such as BERT
and GPT2 can consistently identify and reflect personality markers in those
contexts. This behavior illustrates an ability to be manipulated in a highly
predictable way, and frames them as tools for identifying personality traits
and controlling personas in applications such as dialog systems. We also
contribute a crowd-sourced data-set of personality descriptions of human
subjects paired with their `Big Five' personality assessment data, and a
data-set of personality descriptions collated from Reddit.
