@inproceedings{cheng-etal-2025-dehumanizing,
    title = "Dehumanizing Machines: Mitigating Anthropomorphic Behaviors in Text Generation Systems",
    author = "Cheng, Myra  and
      Blodgett, Su Lin  and
      DeVrio, Alicia  and
      Egede, Lisa  and
      Olteanu, Alexandra",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1259/",
    pages = "25923--25948",
    ISBN = "979-8-89176-251-0",
    abstract = "As text generation systems' outputs are increasingly anthropomorphic{---}perceived as human-like{---}scholars have also increasingly raised 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 crowdsourcing 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."
}