Differentiating generated and human-written content is increasingly difficult. We examine how an incentive to convey humanness and task characteristics shape this human vs AI race across five studies. In Study 1-2 (n=530 and n=610) humans and a large language model (LLM) wrote relationship advice or relationship descriptions, either with or without instructions to sound human. New participants (n=428 and n=408) judged each text's source. Instructions to sound human were only effective for the LLM, reducing the human advantage. Study 3 (n=360 and n=350) showed that these effects persist when writers were instructed to avoid sounding like an LLM. Study 4 (n=219) tested empathy as mechanism of humanness and concluded that LLMs can produce empathy without humanness and humanness without empathy. Finally, computational text analysis (Study 5) indicated that LLMs become more human-like by applying an implicit representation of humanness to mimic stochastic empathy.

翻译：区分生成内容与人类撰写内容正变得日益困难。本研究通过五项实验，考察了传达人性特征的激励因素与任务特性如何影响这场人类与人工智能的竞赛。在研究1-2（样本量分别为530和610）中，人类与大型语言模型（LLM）分别撰写恋爱建议或关系描述文本，其中部分作者被要求"听起来像人类"。新参与者（样本量分别为428和408）对每篇文本的来源进行判断。结果显示，"模仿人类"的指令仅对LLM有效，缩小了人类作者的优势。研究3（样本量分别为360和350）表明，当作者被要求"避免听起来像LLM"时，上述效应仍然存在。研究4（样本量219）将共情作为人性特质的机制进行检验，发现LLM能够在不体现人性的情况下展现共情，也能在不表现共情的情况下呈现人性特征。最终，计算文本分析（研究5）表明，LLM通过应用对人性特征的隐式表征来模拟随机共情，从而变得更像人类。