Can Deception Detection Go Deeper? Dataset, Evaluation, and Benchmark for Deception Reasoning

Deception detection has attracted increasing attention due to its importance in many practical scenarios. Currently, data scarcity harms the development of this field. On the one hand, it is costly to hire participants to simulate deception scenarios. On the other hand, it is difficult to collect videos containing deceptive behaviors on the Internet. To address data scarcity, this paper proposes a new data collection pipeline. Specifically, we use GPT-4 to simulate a role-play between a suspect and a police officer. During interrogation, the suspect lies to the police officer to evade responsibility for the crime, while the police officer uncovers the truth and gathers evidence. Compared with previous datasets, this strategy reduces data collection costs, providing a promising way to increase the dataset size. Meanwhile, we extend the traditional deception detection task to deception reasoning, further providing evidence for deceptive parts. This dataset can also be used to evaluate the complex reasoning capability of current large language models and serve as a reasoning benchmark for further research.