  Large language models (LLMs) are currently at the forefront of intertwining
artificial intelligence (AI) systems with human communication and everyday
life. Thus, aligning them with human values is of great importance. However,
given the steady increase in reasoning abilities, future LLMs are under
suspicion of becoming able to deceive human operators and utilizing this
ability to bypass monitoring efforts. As a prerequisite to this, LLMs need to
possess a conceptual understanding of deception strategies. This study reveals
that such strategies emerged in state-of-the-art LLMs, such as GPT-4, but were
non-existent in earlier LLMs. We conduct a series of experiments showing that
state-of-the-art LLMs are able to understand and induce false beliefs in other
agents, that their performance in complex deception scenarios can be amplified
utilizing chain-of-thought reasoning, and that eliciting Machiavellianism in
LLMs can alter their propensity to deceive. In sum, revealing hitherto unknown
machine behavior in LLMs, our study contributes to the nascent field of machine
psychology.
