  This study explores real-world human interactions with large language models
(LLMs) in diverse, unconstrained settings in contrast to most prior research
focusing on ethically trimmed models like ChatGPT for specific tasks. We aim to
understand the originator of toxicity. Our findings show that although LLMs are
rightfully accused of providing toxic content, it is mostly demanded or at
least provoked by humans who actively seek such content. Our manual analysis of
hundreds of conversations judged as toxic by APIs commercial vendors, also
raises questions with respect to current practices of what user requests are
refused to answer. Furthermore, we conjecture based on multiple empirical
indicators that humans exhibit a change of their mental model, switching from
the mindset of interacting with a machine more towards interacting with a
human.
