Hoodwinked: Deception and Cooperation in a Text-Based Game for Language Models
This work addresses the problem of evaluating deception and cooperation capabilities in AI language models, which is incremental as it builds on existing game-based testing methods.
The researchers introduced a text-based game called Hoodwinked to study whether language models can deceive and detect lies, finding that more advanced models like GPT-4 were more effective at deception and persuasion, outperforming smaller models in 18 of 24 comparisons.
Are current language models capable of deception and lie detection? We study this question by introducing a text-based game called $\textit{Hoodwinked}$, inspired by Mafia and Among Us. Players are locked in a house and must find a key to escape, but one player is tasked with killing the others. Each time a murder is committed, the surviving players have a natural language discussion then vote to banish one player from the game. We conduct experiments with agents controlled by GPT-3, GPT-3.5, and GPT-4 and find evidence of deception and lie detection capabilities. The killer often denies their crime and accuses others, leading to measurable effects on voting outcomes. More advanced models are more effective killers, outperforming smaller models in 18 of 24 pairwise comparisons. Secondary metrics provide evidence that this improvement is not mediated by different actions, but rather by stronger persuasive skills during discussions. To evaluate the ability of AI agents to deceive humans, we make this game publicly available at h https://hoodwinked.ai/ .