  Have Large Language Models (LLMs) developed a personality? The short answer
is a resounding "We Don't Know!". In this paper, we show that we do not yet
have the right tools to measure personality in language models. Personality is
an important characteristic that influences behavior. As LLMs emulate
human-like intelligence and performance in various tasks, a natural question to
ask is whether these models have developed a personality. Previous works have
evaluated machine personality through self-assessment personality tests, which
are a set of multiple-choice questions created to evaluate personality in
humans. A fundamental assumption here is that human personality tests can
accurately measure personality in machines. In this paper, we investigate the
emergence of personality in five LLMs of different sizes ranging from 1.5B to
30B. We propose the Option-Order Symmetry property as a necessary condition for
the reliability of these self-assessment tests. Under this condition, the
answer to self-assessment questions is invariant to the order in which the
options are presented. We find that many LLMs personality test responses do not
preserve option-order symmetry. We take a deeper look at LLMs test responses
where option-order symmetry is preserved to find that in these cases, LLMs do
not take into account the situational statement being tested and produce the
exact same answer irrespective of the situation being tested. We also identify
the existence of inherent biases in these LLMs which is the root cause of the
aforementioned phenomenon and makes self-assessment tests unreliable. These
observations indicate that self-assessment tests are not the correct tools to
measure personality in LLMs. Through this paper, we hope to draw attention to
the shortcomings of current literature in measuring personality in LLMs and
call for developing tools for machine personality measurement.
