AnthroScore：一种计算语言学的拟人化度量
AnthroScore: A Computational Linguistic Measure of Anthropomorphism
AI总结：
提出AnthroScore，一种基于掩码语言模型的自动拟人化度量，验证其与人类判断一致，并应用于15年论文和新闻，发现拟人化随时间增长且语言模型相关论文最高，新闻标题高于论文。
摘要：
拟人化，即将人类特征赋予非人类实体，一直影响着关于技术影响和可能性的讨论。我们提出了AnthroScore，一种自动度量语言中隐含拟人化的指标。我们使用掩码语言模型来量化非人类实体如何被周围语境隐含地框架为人类。我们展示了AnthroScore与人类对拟人化的判断以及社会科学文献中描述的拟人化维度相对应。出于对计算机科学话语中误导性拟人化的担忧，我们使用AnthroScore分析了15年的研究论文和下游新闻文章。在研究论文中，我们发现拟人化随时间稳步增加，且与语言模型相关的论文拟人化程度最高。在ACL论文中，拟人化的时间增长与关键神经进展相关。基于对大众媒体中科学错误信息的担忧，我们识别出新闻标题中的拟人化水平高于其引用的研究论文。由于AnthroScore无需词典，它可直接应用于广泛的文本来源。
英文摘要：
Anthropomorphism, or the attribution of human-like characteristics to non-human entities, has shaped conversations about the impacts and possibilities of technology. We present AnthroScore, an automatic metric of implicit anthropomorphism in language. We use a masked language model to quantify how non-human entities are implicitly framed as human by the surrounding context. We show that AnthroScore corresponds with human judgments of anthropomorphism and dimensions of anthropomorphism described in social science literature. Motivated by concerns of misleading anthropomorphism in computer science discourse, we use AnthroScore to analyze 15 years of research papers and downstream news articles. In research papers, we find that anthropomorphism has steadily increased over time, and that papers related to language models have the most anthropomorphism. Within ACL papers, temporal increases in anthropomorphism are correlated with key neural advancements. Building upon concerns of scientific misinformation in mass media, we identify higher levels of anthropomorphism in news headlines compared to the research papers they cite. Since AnthroScore is lexicon-free, it can be directly applied to a wide range of text sources.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
