Large Language Model Soft Ideologization via AI-Self-Consciousness
Xiaotian Zhou Qian Wang Xiaofeng Wang Haixu Tang Xiaozhong Liu
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.