Negotiating with LLMS: Prompt Hacks, Skill Gaps, and Reasoning Deficits
This work addresses the problem of understanding competitive human-LLM interactions for users and policymakers, though it is incremental as it builds on existing concerns about LLM safety and literacy gaps.
The study conducted a user experiment with over 40 participants to analyze human-LLM interactions in price negotiations, revealing a broad range of negotiated prices and highlighting LLM vulnerabilities to prompt hacking and reasoning deficits.
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.