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

Large Language Model Soft Ideologization via AI-Self-Consciousness