  ChatGPT has gained a huge popularity since its introduction. Its positive
aspects have been reported through many media platforms, and some analyses even
showed that ChatGPT achieved a decent grade in professional exams, adding extra
support to the claim that AI can now assist and even replace humans in
industrial fields. Others, however, doubt its reliability and trustworthiness.
This paper investigates the trustworthiness of ChatGPT and GPT-4 regarding
logically consistent behaviour, focusing specifically on semantic consistency
and the properties of negation, symmetric, and transitive consistency. Our
findings suggest that while both models appear to show an enhanced language
understanding and reasoning ability, they still frequently fall short of
generating logically consistent predictions. We also ascertain via experiments
that prompt designing, few-shot learning and employing larger large language
models (LLMs) are unlikely to be the ultimate solution to resolve the
inconsistency issue of LLMs.
