Can Deception Detection Go Deeper? Dataset, Evaluation, and Benchmark
  for Deception Reasoning
Kang Chen Zheng Lian Haiyang Sun Bin Liu Jianhua Tao
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.