Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.23219v1</id>\n    <title>Decoding AI Authorship: Can LLMs Truly Mimic Human Style Across Literature and Politics?</title>\n    <updated>2026-03-24T13:58:09Z</updated>\n    <link href='https://arxiv.org/abs/2603.23219v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.23219v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>Amidst the rising capabilities of generative AI to mimic specific human styles, this study investigates the ability of state-of-the-art large language models (LLMs), including GPT-4o, Gemini 1.5 Pro, and Claude Sonnet 3.5, to emulate the authorial signatures of prominent literary and political figures: Walt Whitman, William Wordsworth, Donald Trump, and Barack Obama. Utilizing a zero-shot prompting framework with strict thematic alignment, we generated synthetic corpora evaluated through a complementary framework combining transformer-based classification (BERT) and interpretable machine learning (XGBoost). Our methodology integrates Linguistic Inquiry and Word Count (LIWC) markers, perplexity, and readability indices to assess the divergence between AI-generated and human-authored text. Results demonstrate that AI-generated mimicry remains highly detectable, with XGBoost models trained on a restricted set of eight stylometric features achieving accuracy comparable to high-dimensional neural classifiers. Feature importance analyses identify perplexity as the primary discriminative metric, revealing a significant divergence in the stochastic regularity of AI outputs compared to the higher variability of human writing. While LLMs exhibit distributional convergence with human authors on low-dimensional heuristic features, such as syntactic complexity and readability, they do not yet fully replicate the nuanced affective density and stylistic variance inherent in the human-authored corpus. By isolating the specific statistical gaps in current generative mimicry, this study provides a comprehensive benchmark for LLM stylistic behavior and offers critical insights for authorship attribution in the digital humanities and social media.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.CL'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n    <published>2026-03-24T13:58:09Z</published>\n    <arxiv:comment>Preprint. Accepted for publication in Digital Scholarship in the Humanities (OUP)</arxiv:comment>\n    <arxiv:primary_category term='cs.CL'/>\n    <author>\n      <name>Nasser A Alsadhan</name>\n    </author>\n  </entry>"
}