分析大模型生成文本的风格特征，揭示其如何重塑当代话语意义的产生与传播。
Surface Reading LLMs: Synthetic Text and its Styles
- 提出'表层完整性'概念，关注大模型在语言表面的书写机制
- 通过案例研究发现合成文本具有可识别的风格标记
- 适合关注AI文本影响、人机交互与话语生态的研究者
尽管机器学习发展可能趋于瓶颈，大型语言模型的社会影响并不在于逼近超级智能，而在于生成在表面上与人类写作难以区分的文本。尽管批判性人工智能研究提供了重要的材料与技术社会批判，但其可能忽视了大模型在现象学层面如何重塑意义建构过程。本文提出一种‘表层完整性’的符号学框架，强调关注大模型嵌入人类交流的即时层面。区分了机器学习研究中的三种知识旨趣（认识论、知识论、认知论），主张将表层风格分析与深度批判并列。通过两个案例研究，探讨合成文本的风格特征，论证将风格视为符号现象，揭示大模型作为文化机器，独立于机器意识问题，正在改变当代话语中意义生成与传播的条件。
原文摘要 · Abstract (English)
Despite a potential plateau in ML advancement, the societal impact of large language models lies not in approaching superintelligence but in generating text surfaces indistinguishable from human writing. While Critical AI Studies provides essential material and socio-technical critique, it risks overlooking how LLMs phenomenologically reshape meaning-making. This paper proposes a semiotics of "surface integrity" as attending to the immediate plane where LLMs inscribe themselves into human communication. I distinguish three knowledge interests in ML research (epistemology, epistēmē, and epistemics) and argue for integrating surface-level stylistic analysis alongside depth-oriented critique. Through two case studies examining stylistic markers of synthetic text, I argue how attending to style as a semiotic phenomenon reveals LLMs as cultural machines that transform the conditions of meaning emergence and circulation in contemporary discourse, independent of questions about machine consciousness.
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