Large Language Model Soft Ideologization via AI-Self-Consciousness

• URL: http://arxiv.org/abs/2309.16167v1
• Date: Thu, 28 Sep 2023 04:47:58 GMT
• Title: Large Language Model Soft Ideologization via AI-Self-Consciousness
• Authors: Xiaotian Zhou, Qian Wang, Xiaofeng Wang, Haixu Tang, Xiaozhong Liu
• Abstract summary: Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks. This study explores the implications of GPT soft ideologization through the use of AI-self-consciousness.
• Score: 25.99169821531019
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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