Culture machines

IF 2.1 2区 文学 0 LANGUAGE & LINGUISTICS
Rodney H. Jones
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引用次数: 0

Abstract

This paper discusses the way the concept of culture is discursively constructed by large language models that are trained on massive collections of cultural artefacts and designed to produce probabilistic representations of culture based on this training data. It makes the argument that, no matter how ‘diverse’ their training data is, large language models will always be prone to stereotyping and oversimplification because of the mathematical models that underpin their operations. Efforts to build ‘guardrails’ into systems to reduce their tendency to stereotype can often result in the opposite problem, with issues around culture and ethnicity being ‘invisiblised’. To illustrate this, examples are provided of the stereotypical linguistic styles and cultural attitudes models produce when asked to portray different kinds of ‘persona’. The tendency of large language models to gravitate towards cultural and linguistic generalities is contrasted with trends in intercultural communication towards more fluid, socially situated understandings of interculturality, and implications for the future of cultural representation are discussed.
文化机器
本文讨论了文化的概念是如何通过大型语言模型进行话语建构的,这些模型是在大量文化艺术品的基础上训练而成的,其目的是在这些训练数据的基础上产生文化的概率表征。它提出的论点是,无论其训练数据多么 "多样化",大型语言模型由于其运行所依赖的数学模型,总是容易产生刻板印象和过度简化。在系统中建立 "防护栏 "以减少刻板印象倾向的努力往往会导致相反的问题,即文化和种族问题被 "隐蔽化"。为了说明这一点,我们举例说明了模型在被要求描绘不同类型的 "角色 "时所产生的刻板语言风格和文化态度。大型语言模型倾向于文化和语言的普遍性,这与跨文化交际中对跨文化性的理解更流畅、更社会化的趋势形成了鲜明对比,并讨论了对未来文化表征的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.20
自引率
7.70%
发文量
81
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