  Technology for open-ended language generation, a key application of
artificial intelligence, has advanced to a great extent in recent years.
Large-scale language models, which are trained on large corpora of text, are
being used in a wide range of applications everywhere, from virtual assistants
to conversational bots. While these language models output fluent text,
existing research shows that these models can and do capture human biases. Many
of these biases, especially those that could potentially cause harm, are being
well-investigated. On the other hand, studies that infer and change human
personality traits inherited by these models have been scarce or non-existent.
Our work seeks to address this gap by exploring the personality traits of
several large-scale language models designed for open-ended text generation and
the datasets used for training them. We build on the popular Big Five factors
and develop robust methods that quantify the personality traits of these models
and their underlying datasets. In particular, we trigger the models with a
questionnaire designed for personality assessment and subsequently classify the
text responses into quantifiable traits using a Zero-shot classifier. Our
estimation scheme sheds light on an important anthropomorphic element found in
such AI models and can help stakeholders decide how they should be applied as
well as how society could perceive them. Additionally, we examined approaches
to alter these personalities, adding to our understanding of how AI models can
be adapted to specific contexts.
