Detection of incorrect LLM information: It is well established that LLMs can gen-
erate convincing but incorrect responses and content [143]. One of the methods used to
determine if a piece of text about a world fact is generated by an LLM (and hence might need
to be vetted) is to use the predictable traits, lack of human-like personality, and linguistic
features in the LLM-generated language [144, 145]. However, with personality shaping, that
method may be rendered ineffective, thereby making it easier for adversaries to use LLMs to
generate misleading content. The solution for this problem, while out of scope of this work,
is related to solving the broader alignment and grounding of LLMs—an area that should
continue to be explored further in industry and academia.

7 Conclusion
PersonalityTraitsinLargeLanguageModels