Beyond the surface: stylometric analysis of GPT-4o's capacity for literary style imitation.
This study aims to explore the ability of GPT-4o to imitate the literary style of renowned authors. Ernest Hemingway and Mary Shelley were selected due to their contrasting literary styles and their overall impact on world literature. Using three distinct prompting strategies—zero-shot generation, z...
| Publicado en: | Digital Scholarship in the Humanities Vol. 40; no. 2; pp. 587 - 601 |
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| Formato: | Artículo |
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Oxford University Press / USA
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=186085076&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 186085076 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Jun2025 vid: 40 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 186085076 10.1093/llc/fqaf035 ppf: 587 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.2MB tig: atl: Beyond the surface: stylometric analysis of GPT-4o's capacity for literary style imitation. aug: au: Mikros, George affil: Department of Middle Eastern Studies, Hamad Bin Khalifa University, LAS Building, P.O. Box. 34110 Education City, Doha, Qatar su: Language models Attribution of authorship Generative pre-trained transformers Literary style Literature sug: subj: Language models Attribution of authorship Generative pre-trained transformers Literary style Literature keyword: authorship attribution GPT-4o in-context learning large language models (LLMs) stylistic imitation stylometric analysis ab: This study aims to explore the ability of GPT-4o to imitate the literary style of renowned authors. Ernest Hemingway and Mary Shelley were selected due to their contrasting literary styles and their overall impact on world literature. Using three distinct prompting strategies—zero-shot generation, zero-shot imitation, and in-context learning—we generated forty-five stylistic imitations and analyzed them alongside the authors' original texts. To ensure thematic consistency, we constrained the generated texts to shared narrative themes derived from the authors' works. We used a distance-based approach to authorship attribution using the 1,000 most frequent words and cosine distance to explore how the large language model's imitations were positioned in the multidimensional authorship space. Moreover, we exploited a random forest classifier and repeated the authorship attribution task to analyze the authorship distinctiveness of the GPT imitations further. We used a combination of Textual Complexity and Readability, Author Multilevel N-gram Profiles, Word Embeddings, and Linguistic Inquiry and Word Count features. t-SNE visualizations further evaluated the stylistic alignment between original and GPT-generated texts. The findings reveal that while GPT-4o captures some surface-level stylistic elements of the authors, it struggles to fully replicate the depth and uniqueness of their stylometric signatures. Imitations generated via in-context learning showed improved alignment with the original authors but still exhibited significant overlap with generic GPT outputs. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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