Lies are ubiquitous and often happen in social interactions. However, socially conducted deceptions make it hard to get data since people are unlikely to self-report their intentional deception behaviors, especially malicious ones. Social deduction games, a type of social game where deception is a key gameplay mechanic, can be a good alternative to studying social deceptions. Hence, we utilized large language models' (LLMs) high performance in solving complex scenarios that require reasoning and prompt engineering to detect deceivers in the game of Mafia given only partial information and found such an approach acquired better accuracy than previous BERT-based methods in human data and even surpassed human accuracy. Furthermore, we conducted extensive experiments and analyses to find out the strategies behind LLM's reasoning process so that humans could understand the gist of LLM's strategy.
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