Institute for Creative Technologies, University of Southern California, Los Angeles, CA, United States America.
Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA, United States America.
PLoS One. 2024 Apr 11;19(4):e0301382. doi: 10.1371/journal.pone.0301382. eCollection 2024.
People frequently face decisions that require making inferences about withheld information. The advent of large language models coupled with conversational technology, e.g., Alexa, Siri, Cortana, and the Google Assistant, is changing the mode in which people make these inferences. We demonstrate that conversational modes of information provision, relative to traditional digital media, result in more critical responses to withheld information, including: (1) a reduction in evaluations of a product or service for which information is withheld and (2) an increased likelihood of recalling that information was withheld. These effects are robust across multiple conversational modes: a recorded phone conversation, an unfolding chat conversation, and a conversation script. We provide further evidence that these effects hold for conversations with the Google Assistant, a prominent conversational technology. The experimental results point to participants' intuitions about why the information was withheld as the driver of the effect.
人们经常面临需要根据隐藏信息进行推断的决策。大型语言模型的出现,加上会话技术(如 Alexa、Siri、Cortana 和 Google Assistant),正在改变人们进行这些推断的方式。我们证明,与传统数字媒体相比,会话式信息提供模式会导致对隐藏信息的更批判性反应,包括:(1)对隐藏信息的产品或服务的评价降低;(2)更有可能回忆起信息被隐藏。这些影响在多种会话模式下都是稳健的:记录的电话对话、展开的聊天对话和对话脚本。我们进一步提供了证据表明,这些影响适用于与 Google Assistant 的对话,这是一种流行的会话技术。实验结果指出了参与者对隐藏信息的直觉,认为这是产生影响的原因。