用作者作品微调AI，生成文本比专业写手更受读者欢迎。
Readers Prefer Outputs of AI Trained on Copyrighted Books over Expert Human Writers
- 用作者完整作品微调AI模型，让其模仿特定作家风格。
- 微调后AI文本被读者认为更贴近原作者文风且质量更高。
- 微调成本仅81美元/作者，远低于人类作者报酬，适合版权争议场景。
使用受版权保护的书籍训练AI引发了作者诉讼，但这类模型能否生成高质量、具作者风格的文学文本尚不明确。我们开展了一项预注册研究，比较了28名MFA背景写作者与3个前沿模型（ChatGPT、Claude、Gemini）在450字以内的文本，模仿50位获奖作家的风格。盲测中，MFA读者对仅通过上下文提示生成的AI文本在风格契合度（OR=0.16）和质量（OR=0.13）上均显著偏好人类写作；而普通读者无风格偏好（OR=1.06），但更倾向AI生成内容（质量OR=1.82）。当对ChatGPT进行作者作品微调后，结果反转：MFA读者更青睐AI在风格（OR=8.16）和质量（OR=1.87）上的表现，普通读者偏好更强（风格OR=16.65；质量OR=5.42）。两种读者群体均更偏爱微调后的AI，但读者类型与偏好存在显著交互作用（风格p=0.021，质量p<10^-4）。微调输出极少被检测为AI生成（3%），而提示生成的达97%。中介分析表明，微调消除了可被检测的AI特征，改变了可检测性与偏好之间的关系。尽管未考虑将AI输出转化为出版级文本所需投入，但平均微调成本仅81美元/作者，较典型写作者报酬降低99.7%。作者专属微调实现了非抄录式文本生成，且广受读者欢迎，为版权法第四条合理使用提供了实证支持。
原文摘要 · Abstract (English)
The use of copyrighted books for training AI has sparked lawsuits from authors concerned about AI generating derivative content. Yet whether these models can produce high-quality literary text emulating authors' voices remains unclear. We conducted a preregistered study comparing MFA-trained writers with three frontier models (ChatGPT, Claude, Gemini) writing up to 450-word excerpts emulating 50 award-winning authors' styles. In blind pairwise evaluations by 28 MFA-trained readers and 516 college-educated general readers, AI text from in-context prompting was strongly disfavored by MFA readers for stylistic fidelity (OR=0.16) and quality (OR=0.13), while general readers showed no fidelity preference (OR=1.06) but favored AI for quality (OR=1.82). Fine-tuning ChatGPT on authors' complete works reversed these results: MFA readers favored AI for fidelity (OR=8.16) and quality (OR=1.87), with general readers showing even stronger preference (fidelity OR=16.65; quality OR=5.42). Both groups preferred fine-tuned AI, but the writer-type X reader-type interaction remained significant (p=0.021 for fidelity; p<10^-4 for quality), indicating general readers favored AI by a wider margin. Effects are robust under cluster-robust inference and generalize across authors in heterogeneity analyses. Fine-tuned outputs were rarely flagged as AI-generated (3% vs. 97% for prompting) by leading detectors. Mediation analysis shows fine-tuning eliminates detectable AI quirks that penalize in-context outputs, altering the nexus between detectability and preference. While not accounting for effort to transform AI output into publishable prose, the median fine-tuning cost of $81 per author represents a 99.7% reduction versus typical writer compensation. Author-specific fine-tuning enables non-verbatim AI writing preferred over expert human writing, providing evidence relevant to copyright's fourth fair-use factor.
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