标题： 读者更喜欢由受版权书籍训练的AI生成的输出，而不是专业人类作家的输出 显示英文标题

标题： Readers Prefer Outputs of AI Trained on Copyrighted Books over Expert Human Writers

摘要： 使用受版权保护的书籍来训练人工智能模型，已引发了许多作者的诉讼，这些作者担心人工智能生成衍生内容的能力。 然而，尚不清楚这些模型在模仿作者风格的同时，是否能够生成高质量的文学文本。 为了解答这个问题，我们进行了一项预先注册的研究，将MFA训练的专家作家与三个前沿人工智能模型：ChatGPT、Claude和Gemini进行比较，写作最多450字的段落，模仿50位获奖作者的不同风格。 在159名代表性专家和普通读者的盲测成对评估中，通过上下文提示生成的人工智能文本在风格忠实度（OR=0.16，p<10^-8）和写作质量（OR=0.13，p<10^-7）方面被专家强烈不喜欢，但普通读者的结果则不一致。 然而，对个别作者的全部作品进行微调后，ChatGPT的结果完全逆转：专家现在更倾向于人工智能生成的文本在风格忠实度（OR=8.16，p<10^-13）和写作质量（OR=1.87，p=0.010）方面，普通读者也表现出类似的转变。 这些效果在不同作者和风格中都具有普遍性。 微调后的输出很少被最佳人工智能检测器标记为人工智能生成的（3%的标记率，相比之下上下文提示的标记率为97%）。 中介分析显示，这种逆转发生是因为微调消除了可检测的人工智能风格特点（例如陈词滥调密度），这些特点会惩罚上下文提示的输出。 尽管我们没有考虑将原始人工智能输出转化为连贯、可发表的散文所需的人类努力的额外成本，但每位作者的中位微调和推理成本为81美元，与典型的专业作家薪酬相比，减少了99.7%。 因此，针对特定作者的微调使得读者更喜欢非逐字的人工智能写作，而不是专家级的人类写作，这为版权的第四项合理使用因素提供了直接相关的实证证据，即“对源作品潜在市场或价值的影响”。 显示英文摘要

摘要： The use of copyrighted books for training AI models has led to numerous lawsuits from authors concerned about AI's ability to generate derivative content. Yet it's unclear if these models can generate high quality literary text while emulating authors' styles. To answer this we conducted a preregistered study comparing MFA-trained expert writers with three frontier AI models: ChatGPT, Claude & Gemini in writing up to 450 word excerpts emulating 50 award-winning authors' diverse styles. In blind pairwise evaluations by 159 representative expert & lay readers, AI-generated text from in-context prompting was strongly disfavored by experts for both stylistic fidelity (OR=0.16, p<10^-8) & writing quality (OR=0.13, p<10^-7) but showed mixed results with lay readers. However, fine-tuning ChatGPT on individual authors' complete works completely reversed these findings: experts now favored AI-generated text for stylistic fidelity (OR=8.16, p<10^-13) & writing quality (OR=1.87, p=0.010), with lay readers showing similar shifts. These effects generalize across authors & styles. The fine-tuned outputs were rarely flagged as AI-generated (3% rate v. 97% for in-context prompting) by best AI detectors. Mediation analysis shows this reversal occurs because fine-tuning eliminates detectable AI stylistic quirks (e.g., cliche density) that penalize in-context outputs. While we do not account for additional costs of human effort required to transform raw AI output into cohesive, publishable prose, the median fine-tuning & inference cost of $81 per author represents a dramatic 99.7% reduction compared to typical professional writer compensation. Author-specific fine-tuning thus enables non-verbatim AI writing that readers prefer to expert human writing, providing empirical evidence directly relevant to copyright's fourth fair-use factor, the "effect upon the potential market or value" of the source works.