解码AI作者身份：LLMs能否真正模仿文学和政治领域的作家风格？
Decoding AI Authorship: Can LLMs Truly Mimic Human Style Across Literature and Politics?
AI总结：
研究探讨了先进大语言模型在模仿文学和政治作家风格方面的能力，发现AI生成文本仍易被检测，主要差异在于随机性与情感密度。
摘要：
在生成AI模仿特定人类风格能力日益增强的背景下，本研究探讨了最先进的大型语言模型（如GPT-4o、Gemini 1.5 Pro和Claude Sonnet 3.5）模仿著名文学和政治人物（如沃尔特·惠特曼、威廉·华兹华斯、唐纳德·特朗普和巴拉克·奥巴马）作者风格的能力。通过零样本提示框架和严格主题对齐，我们生成了合成语料库，并通过结合基于变压器的分类（BERT）和可解释机器学习（XGBoost）的互补框架进行评估。我们的方法整合了语言学探究和词频（LIWC）标记、困惑度和可读性指数，以评估AI生成文本与人类写作之间的差异。结果表明，AI生成的模仿仍高度可检测，XGBoost模型在八种风格学特征上训练的准确率与高维神经分类器相当。特征重要性分析发现困惑度是主要的判别指标，揭示了AI输出在随机性上的显著差异，与人类写作的更高变异性相比。尽管LLMs在低维启发式特征（如句法复杂性和可读性）上与人类作者有分布收敛，但尚未完全复制人类写作中细腻的情感密度和风格变异性。通过隔离当前生成模仿中的具体统计差距，本研究为LLM的风格行为提供了全面的基准，并为数字人文和社交媒体中的作者身份认定提供了关键见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
