This study investigates the generation of synthetic disinformation by OpenAI's Large Language Models (LLMs) through prompt engineering and explores their responsiveness to emotional prompting. Leveraging various LLM iterations using davinci-002, davinci-003, gpt-3.5-turbo and gpt-4, we designed experiments to assess their success in producing disinformation. Our findings, based on a corpus of 19,800 synthetic disinformation social media posts, reveal that all LLMs by OpenAI can successfully produce disinformation, and that they effectively respond to emotional prompting, indicating their nuanced understanding of emotional cues in text generation. When prompted politely, all examined LLMs consistently generate disinformation at a high frequency. Conversely, when prompted impolitely, the frequency of disinformation production diminishes, as the models often refuse to generate disinformation and instead caution users that the tool is not intended for such purposes. This research contributes to the ongoing discourse surrounding responsible development and application of AI technologies, particularly in mitigating the spread of disinformation and promoting transparency in AI-generated content.

翻译：本研究探讨了OpenAI的大语言模型（LLMs）通过提示工程生成合成虚假信息的情况，并分析了它们对情感提示的响应性。利用davinci-002、davinci-003、gpt-3.5-turbo和gpt-4等多个LLM迭代版本，我们设计了实验来评估其在生成虚假信息方面的成功程度。基于包含19,800条合成虚假信息社交媒体帖子的语料库，研究结果揭示：OpenAI的所有LLM均能成功生成虚假信息，并且能有效响应情感提示，表明它们在文本生成中对情感线索具有细致入微的理解。当以礼貌方式提示时，所有受检LLM均以高频率持续生成虚假信息；相反，当以不礼貌方式提示时，虚假信息的生成频率降低，因为模型常常拒绝生成虚假信息，并提醒用户该工具不适用于此类目的。本研究为当前关于AI技术负责任开发与应用（尤其是在遏制虚假信息传播和提升AI生成内容透明度方面）的持续讨论做出了贡献。