使用 ChatGPT 大语言模型模拟 H.P. Lovecraft 恐怖文学
Simulating H.P. Lovecraft horror literature with the ChatGPT large language model
AI总结：
本研究提出利用 ChatGPT（GPT-4架构）结合高级 prompt engineering 技术模拟 H.P. Lovecraft 恐怖文学风格，经本科生问卷调查与统计假设检验证明，参与者无法可靠区分模型生成文本与真实作品，验证了该方法在文学模仿上的有效性。
摘要：
在本文中，我们提出了一种使用 ChatGPT 大语言模型（特别是 GPT-4 架构）模拟 H.P. Lovecraft 恐怖文学的新方法。我们的研究旨在生成模仿 Lovecraft 独特写作风格和主题的文本，同时检验 prompt engineering 技术在引导模型输出方面的有效性。为此，我们精心策划了一个包含多篇专业文献参考的 prompt，并采用了高级 prompt engineering 方法。我们通过向一组本科生样本发放调查问卷，对生成的文本进行了实证评估。利用统计假设检验，我们评估了学生区分真正 Lovecraft 作品与模型生成作品的能力。研究结果表明，参与者无法可靠地区分两者，这证明了 GPT-4 模型及我们的 prompt engineering 技术在模仿 Lovecraft 文学风格方面的有效性。除了展示 GPT 模型的能力外，本文还全面描述了其底层架构，并与模拟其他著名作者和哲学家（如 Dennett）的相关工作进行了比较分析。通过探索大语言模型在文学模仿背景下的潜力，我们的研究为这些模型在各种创意领域的应用与局限性研究做出了贡献。
英文摘要：
In this paper, we present a novel approach to simulating H.P. Lovecraft's horror literature using the ChatGPT large language model, specifically the GPT-4 architecture. Our study aims to generate text that emulates Lovecraft's unique writing style and themes, while also examining the effectiveness of prompt engineering techniques in guiding the model's output. To achieve this, we curated a prompt containing several specialized literature references and employed advanced prompt engineering methods. We conducted an empirical evaluation of the generated text by administering a survey to a sample of undergraduate students. Utilizing statistical hypothesis testing, we assessed the students ability to distinguish between genuine Lovecraft works and those generated by our model. Our findings demonstrate that the participants were unable to reliably differentiate between the two, indicating the effectiveness of the GPT-4 model and our prompt engineering techniques in emulating Lovecraft's literary style. In addition to presenting the GPT model's capabilities, this paper provides a comprehensive description of its underlying architecture and offers a comparative analysis with related work that simulates other notable authors and philosophers, such as Dennett. By exploring the potential of large language models in the context of literary emulation, our study contributes to the body of research on the applications and limitations of these models in various creative domains.
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。
