As text generation systems' outputs are increasingly anthropomorphic -- perceived as human-like -- scholars have also raised increasing concerns about how such outputs can lead to harmful outcomes, such as users over-relying or developing emotional dependence on these systems. How to intervene on such system outputs to mitigate anthropomorphic behaviors and their attendant harmful outcomes, however, remains understudied. With this work, we aim to provide empirical and theoretical grounding for developing such interventions. To do so, we compile an inventory of interventions grounded both in prior literature and a crowdsourced study where participants edited system outputs to make them less human-like. Drawing on this inventory, we also develop a conceptual framework to help characterize the landscape of possible interventions, articulate distinctions between different types of interventions, and provide a theoretical basis for evaluating the effectiveness of different interventions.

本研究解决了文本生成系统输出的拟人化行为可能导致用户过度依赖和情感依赖等有害后果的问题。通过编制干预措施的清单并建立概念框架，本文为如何减轻这些行为提供了理论和实证基础。研究的主要发现是这一框架可以帮助识别和评估不同干预措施的有效性，潜在影响深远。

去人性化机器：减轻文本生成系统中的拟人化行为