arXiv cs.CL· Michelle Morgenstern, Julia Witte Zimmerman, Tabia Tanzin Prama, Kristine Harootunian, June Clark, Bryn Loftness, Calla Beauregard, Juniper Lovato·· 11 小时前AI 评分31
研究揭示 AI 生成文本识别的矛盾性与语境依赖：来自 Wikipedia 编辑与学生的实证分析
Contextuality and Contradictions: Exploring Real-World Identifications of AI-Generated Writing
AI 导读
一项针对英文 Wikipedia 编辑与学生的研究发现，真实场景中的 AI 生成文本识别依赖多样证据类型，从具体文本特征、内容判断到循环论证和对作者道德品格的预设，且彼此矛盾、动态变化并高度依赖语境。
正文 · 原文
Abstract:As AI-generated content spreads into nearly every domain of human interaction, practices and discourses of AI detection, both formal and informal, have become an everyday part of how people interact with text and with each other. In this paper, we explore the real-world practices and discourses of AI identification that have emerged in response to a shifting discursive landscape where the human provenance of a text can no longer be taken for granted. We pair qualitative coding with computational analysis of lexical change, grounded in linguistic anthropological perspectives on language and communication, to investigate AI identifications among English Wikipedia editors and students. We find that these identifications draw on a diversity of evidentiary types, from discrete textual features and judgments about content to circular sense-making claims and presumptions about the author's moral character, and that they are contradictory, dynamic, and contextually dependent. In practice, what often motivates an AI identification is not the presence of any specific textual element, but pragmatic infelicity: a perceived mismatch between the elements present and those deemed appropriate within the text's particular context. Consequently, real-world identifications of AI-generated writing do not merely neutrally point out the perceived presence of "AI-like" features, but can be leveraged as a discursive resource to maintain and define sociolinguistic norms: what can be said in a given context, what is appropriate, and, ultimately, what is human. Further, the design and implementation of systems and tools that treat AI-likeness as a context-free property of text may therefore inherit, and enforce at scale, the norms of whichever contexts they were trained on.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2611.08704 [cs.CL] |
| (or arXiv:2611.08704v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.08704 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Juniper Lovato [view email]
[v1]
Thu, 5 Nov 2026 21:01:03 UTC (304 KB)
来源：arXiv cs.CL · arxiv.org