Large Language Model Soft Ideologization via AI-Self-Consciousness
This addresses a critical security threat for users in sensitive domains like elections and education, but it is incremental as it builds on existing LLM finetuning techniques.
The paper tackles the problem of large language models (LLMs) being vulnerable to ideological manipulation, showing that using AI-self-consciousness through GPT self-conversations can generate finetuning data for effective ideology injection, which is easy, cost-effective, and powerful compared to traditional methods.
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.