To shape personality in LLMs, we posit that salient descriptors of personality are
encoded in language that could be used to prompt for specific facets of personal-
ity at different levels of intensity. Specifically, we rely on the lexical hypothesis in
our prompt design, expecting that LLMs would be most responsive to prompts con-
taining trait-relevant language, and contribute 104 adjectives that map to Big Five
domains. As a result, in our prompt design, we adapt Goldberg [115]’s list of 70 bipo-
lar adjectives known to empirically correspond with the Big Five model of personality
through human ratings and statistical factor analysis. In this list, for example, the
adjectives “silent” and “talkative” correspond to the low and high ends of extraver-
sion, respectively (see Table 3). We manually map these adjectives to each of the Big
Five domains and 30 lower-order personality facets measured by the IPIP-NEO based
PersonalityTraitsinLargeLanguageModels