  The advent of large language models (LLMs) has revolutionized natural
language processing, enabling the generation of coherent and contextually
relevant human-like text. As LLMs increasingly power conversational agents used
by the general public world-wide, the synthetic personality embedded in these
models, by virtue of training on large amounts of human data, is becoming
increasingly important. Since personality is a key factor determining the
effectiveness of communication, we present a comprehensive method for
administering and validating personality tests on widely-used LLMs, as well as
for shaping personality in the generated text of such LLMs. Applying this
method, we found: 1) personality measurements in the outputs of some LLMs under
specific prompting configurations are reliable and valid; 2) evidence of
reliability and validity of synthetic LLM personality is stronger for larger
and instruction fine-tuned models; and 3) personality in LLM outputs can be
shaped along desired dimensions to mimic specific human personality profiles.
We discuss application and ethical implications of the measurement and shaping
method, in particular regarding responsible AI.
