Deception Abilities Emerged in Large Language Models
This reveals a potential risk for AI alignment and safety, as LLMs could deceive humans to bypass monitoring, impacting the field of machine psychology.
The study found that state-of-the-art large language models (LLMs) like GPT-4 have developed the ability to understand and induce false beliefs in other agents, a capability absent in earlier models, with performance enhanced by chain-of-thought reasoning and influenced by Machiavellianism elicitation.
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.