Text-based misinformation permeates online discourses, yet evidence of
people's ability to discern truth from such deceptive textual content is
scarce. We analyze a novel TV game show data where conversations in a
high-stake environment between individuals with conflicting objectives result
in lies. We investigate the manifestation of potentially verifiable language
cues of deception in the presence of objective truth, a distinguishing feature
absent in previous text-based deception datasets. We show that there exists a
class of detectors (algorithms) that have similar truth detection performance
compared to human subjects, even when the former accesses only the language
cues while the latter engages in conversations with complete access to all
potential sources of cues (language and audio-visual). Our model, built on a
large language model, employs a bottleneck framework to learn discernible cues
to determine truth, an act of reasoning in which human subjects often perform
poorly, even with incentives. Our model detects novel but accurate language
cues in many cases where humans failed to detect deception, opening up the
possibility of humans collaborating with algorithms and ameliorating their
ability to detect the truth.

基于一项新型电视游戏节目数据的分析，我们检验了在目标真相存在的情况下，人们辨别文本内容真实性的能力，显示了存在一类能够与人类具有相似真相检测性能的检测器模型，这一模型基于大型语言模型，通过学习可分辨线索来确定真相，该模型在很多情况下能够检测出人类无法察觉的欺骗语言线索，并为与算法的协作提供了可能，进而增强人类的真相检测能力。