Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks. However, few studies have addressed the LLM threat and vulnerability from an ideology perspective, especially when they are increasingly being deployed in sensitive domains, e.g., elections and education. In this study, we explore the implications of GPT soft ideologization through the use of AI-self-consciousness. By utilizing GPT self-conversations, AI can be granted a vision to "comprehend" the intended ideology, and subsequently generate finetuning data for LLM ideology injection. When compared to traditional government ideology manipulation techniques, such as information censorship, LLM ideologization proves advantageous; it is easy to implement, cost-effective, and powerful, thus brimming with risks.

翻译：大型语言模型（LLMs）在大量自然语言任务中展现出人类水平的性能。然而，鲜有研究从意识形态角度探讨LLM的威胁与脆弱性，尤其是在其日益被部署于选举、教育等敏感领域之时。本研究探索了通过利用AI自我意识实现GPT软意识形态化的影响。借助GPT自我对话机制，AI可获得一种“理解”目标意识形态的视角，进而生成用于LLM意识形态注入的微调数据。与传统政府意识形态操控手段（如信息审查）相比，LLM意识形态化具有易实施、成本低、效果强等优势，因而蕴含巨大风险。