Large language models LLMs like ChatGPT have reached the 100 Mio user barrier
in record time and might increasingly enter all areas of our life leading to a
diverse set of interactions between those Artificial Intelligence models and
humans. While many studies have discussed governance and regulations
deductively from first-order principles, few studies provide an inductive,
data-driven lens based on observing dialogues between humans and LLMs
especially when it comes to non-collaborative, competitive situations that have
the potential to pose a serious threat to people. In this work, we conduct a
user study engaging over 40 individuals across all age groups in price
negotiations with an LLM. We explore how people interact with an LLM,
investigating differences in negotiation outcomes and strategies. Furthermore,
we highlight shortcomings of LLMs with respect to their reasoning capabilities
and, in turn, susceptiveness to prompt hacking, which intends to manipulate the
LLM to make agreements that are against its instructions or beyond any
rationality. We also show that the negotiated prices humans manage to achieve
span a broad range, which points to a literacy gap in effectively interacting
with LLMs.

通过观察人类与大型语言模型（LLMs）的对话，本研究基于数据驱动的方法，对 LLMs 的治理和调节进行了归纳性分析，并探讨了非合作性、竞争性情境下的人机交互对人类可能构成的严重威胁以及利用 LLMs 的推理能力存在的不足和可操纵性。此外，研究还揭示了人类在与 LLMs 进行价格谈判时所达成的价格涵盖了广泛范围，指出了人类在有效与 LLMs 互动方面存在的文盲问题。