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.

GPT 软意识使人工智能能够理解预期的意识形态，并生成用于注入大型语言模型意识形态的微调数据，相比传统的政府意识形态操控技术，如信息审查，LLM 意识形态化具有易实施、低成本和高效的优势，但也存在潜在风险。