Decoding AI Authorship: Can LLMs Truly Mimic Human Style Across Literature and Politics?

• URL: http://arxiv.org/abs/2603.23219v1
• Date: Tue, 24 Mar 2026 13:58:09 GMT
• Title: Decoding AI Authorship: Can LLMs Truly Mimic Human Style Across Literature and Politics?
• Abstract summary: This study investigates the ability of state-of-the-art large language models (LLMs) to emulate the authorial signatures of prominent literary and political figures.<n>We generated synthetic corpora evaluated through a complementary framework combining transformer-based classification (BERT) and interpretable machine learning (XGBoost)<n>Our methodology integrates Linguistic Inquiry and Word Count markers, perplexity, and readability indices to assess the divergence between AI-generated and human-authored text.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Is AI Catching Up to Human Expression? Exploring Emotion, Personality, Authorship, and Linguistic Style in English and Arabic with Six Large Language Models [0.0]
  We conduct two tasks across six models:Jais, Mistral, LLaMA, GPT-4o, Gemini, and DeepSeek.<n>We evaluate whether machine classifiers can reliably distinguish between human-authored and AI-generated texts.<n>Our results demonstrate that AI-generated texts are distinguishable from human-authored ones (F1>0.95), though classification performance deteriorates on paraphrased samples.<n> augmenting training with AI-generated data enhances performance in the Arabic personality classification task, highlighting the potential of synthetic data to address challenges in under-resourced languages.
  arXiv  Detail & Related papers  (2026-03-24T14:17:02Z)
• Large Language Models and Forensic Linguistics: Navigating Opportunities and Threats in the Age of Generative AI [0.0]
  Large language models (LLMs) serve as powerful analytical tools enabling scalable corpus analysis and embedding-based authorship attribution.<n>Recent stylometric research indicates that LLMs can approximate surface stylistic features yet exhibit detectable differences from human writers.<n>The article concludes that forensic linguistics requires methodological reconfiguration to remain scientifically credible and legally admissible.
  arXiv  Detail & Related papers  (2025-12-07T17:05:31Z)
• Computational Turing Test Reveals Systematic Differences Between Human and AI Language [0.0]
  Large language models (LLMs) are increasingly used in the social sciences to simulate human behavior.<n>Existing validation efforts rely heavily on human-judgment-based evaluations.<n>This paper introduces a computational Turing test to assess how closely LLMs approximate human language.
  arXiv  Detail & Related papers  (2025-11-06T08:56:37Z)
• TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation [55.55404595177229]
  Large Language Models (LLMs) are exhibiting emergent human-like abilities.<n>TwinVoice is a benchmark for assessing persona simulation across diverse real-world contexts.
  arXiv  Detail & Related papers  (2025-10-29T14:00:42Z)
• StyleDecipher: Robust and Explainable Detection of LLM-Generated Texts with Stylistic Analysis [18.44456241158174]
  StyleDecipher is a robust and explainable detection framework.<n>It revisits text detection using combined feature extractors to quantify stylistic differences.<n>It consistently achieves state-of-the-art in-domain accuracy.
  arXiv  Detail & Related papers  (2025-10-14T15:07:27Z)
• Human-like Content Analysis for Generative AI with Language-Grounded Sparse Encoders [46.13876748421428]
  Language-Grounded Sparses (LanSE) decomposes images into interpretable visual patterns with natural language descriptions.<n>Our method discovers more than 5,000 visual patterns with 93% human agreement.<n>Our method's capability to extract language-grounded patterns can be naturally adapted to numerous fields.
  arXiv  Detail & Related papers  (2025-08-20T06:50:15Z)
• ForenX: Towards Explainable AI-Generated Image Detection with Multimodal Large Language Models [82.04858317800097]
  We present ForenX, a novel method that not only identifies the authenticity of images but also provides explanations that resonate with human thoughts.<n>ForenX employs the powerful multimodal large language models (MLLMs) to analyze and interpret forensic cues.<n>We introduce ForgReason, a dataset dedicated to descriptions of forgery evidences in AI-generated images.
  arXiv  Detail & Related papers  (2025-08-02T15:21:26Z)
• From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning [63.25540801694765]
  Large Language Models (LLMs) demonstrate striking linguistic abilities, yet whether they achieve this same balance remains unclear.<n>We apply the Information Bottleneck principle to quantitatively compare how LLMs and humans navigate this compression-meaning trade-off.
  arXiv  Detail & Related papers  (2025-05-21T16:29:00Z)
• Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents [55.63497537202751]
  Article explores the convergence of connectionist and symbolic artificial intelligence (AI) Traditionally, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic. Recent advancements in large language models (LLMs) highlight the potential of connectionist architectures in handling human language as a form of symbols.
  arXiv  Detail & Related papers  (2024-07-11T14:00:53Z)
• Inclusivity in Large Language Models: Personality Traits and Gender Bias in Scientific Abstracts [49.97673761305336]
  We evaluate three large language models (LLMs) for their alignment with human narrative styles and potential gender biases. Our findings indicate that, while these models generally produce text closely resembling human authored content, variations in stylistic features suggest significant gender biases.
  arXiv  Detail & Related papers  (2024-06-27T19:26:11Z)
• Neural Authorship Attribution: Stylometric Analysis on Large Language Models [16.63955074133222]
  Large language models (LLMs) such as GPT-4, PaLM, and Llama have significantly propelled the generation of AI-crafted text. With rising concerns about their potential misuse, there is a pressing need for AI-generated-text forensics.
  arXiv  Detail & Related papers  (2023-08-14T17:46:52Z)
• Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision [84.31474052176343]
  Recent AI-assistant agents, such as ChatGPT, rely on supervised fine-tuning (SFT) with human annotations and reinforcement learning from human feedback to align the output with human intentions. This dependence can significantly constrain the true potential of AI-assistant agents due to the high cost of obtaining human supervision. We propose a novel approach called SELF-ALIGN, which combines principle-driven reasoning and the generative power of LLMs for the self-alignment of AI agents with minimal human supervision.
  arXiv  Detail & Related papers  (2023-05-04T17:59:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.