Autonomous Agents for Interrogation

Merav Chkroun, Amos Azaria

Abstract: In this paper, we introduce an autonomous agent designed for interrogation. Our methodology includes the development of a text-based game, enabling participants to choose their individual roles. We prompt a Large Language Model to serve as a player in the game, playing with a human participant. The game transcripts serve as a unique dataset, assigning each player's selected role as the ground truth label. We leverage the hidden states of a Large Language Model for participant role detection based on interrogation transcripts. Our approach outperforms other methods in text-based deception detection. Our results underscore the potential viability of autonomous agents in interrogation and deception detection.