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.

翻译：本文提出了一种利用ChatGPT大语言模型（特别是GPT-4架构）模拟H.P.洛夫克拉夫特恐怖文学的新方法。本研究旨在生成模仿洛夫克拉夫特独特写作风格与主题的文本，同时考察提示工程技术在引导模型输出方面的有效性。为实现此目标，我们构建了一个包含多部专业文学参考文献的提示词，并采用了先进的提示工程方法。通过对本科学生样本进行问卷调查，我们开展了生成文本的实证评估。运用统计假设检验，评估了学生区分洛夫克拉夫特原作与模型生成文本的能力。研究结果表明，参与者无法可靠区分两者，这证实了GPT-4模型及我们采用的提示工程技术在模仿洛夫克拉夫特文学风格方面的有效性。除展示GPT模型的能力外，本文还详细阐述了其底层架构，并与模拟丹尼特等其他著名作家与哲学家的相关研究进行了比较分析。通过探索大语言模型在文学模仿语境中的潜力，本研究为关于这些模型在各类创意领域中应用与局限性的研究体系做出了贡献。