  Controversy is a reflection of our zeitgeist, and an important aspect to any
discourse. The rise of large language models (LLMs) as conversational systems
has increased public reliance on these systems for answers to their various
questions. Consequently, it is crucial to systematically examine how these
models respond to questions that pertaining to ongoing debates. However, few
such datasets exist in providing human-annotated labels reflecting the
contemporary discussions. To foster research in this area, we propose a novel
construction of a controversial questions dataset, expanding upon the publicly
released Quora Question Pairs Dataset. This dataset presents challenges
concerning knowledge recency, safety, fairness, and bias. We evaluate different
LLMs using a subset of this dataset, illuminating how they handle controversial
issues and the stances they adopt. This research ultimately contributes to our
understanding of LLMs' interaction with controversial issues, paving the way
for improvements in their comprehension and handling of complex societal
debates.
