Negotiating with LLMS: Prompt Hacks, Skill Gaps, and Reasoning Deficits
Johannes Schneider Steffi Haag Leona Chandra Kruse
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.