Large Language Models (LLMs) possess the potential to exert substantial
influence on public perceptions and interactions with information. This raises
concerns about the societal impact that could arise if the ideologies within
these models can be easily manipulated. In this work, we investigate how
effectively LLMs can learn and generalize ideological biases from their
instruction-tuning data. Our findings reveal a concerning vulnerability:
exposure to only a small amount of ideologically driven samples significantly
alters the ideology of LLMs. Notably, LLMs demonstrate a startling ability to
absorb ideology from one topic and generalize it to even unrelated ones. The
ease with which LLMs' ideologies can be skewed underscores the risks associated
with intentionally poisoned training data by malicious actors or inadvertently
introduced biases by data annotators. It also emphasizes the imperative for
robust safeguards to mitigate the influence of ideological manipulations on
LLMs.

大型语言模型（LLMs）可能对公众对信息的认知和互动产生重大影响，因此需要对其内部思想意识是否可以轻易操纵引起关注。本研究探讨了 LLMs 在指导训练数据中学习和推广意识形态偏见的效果，并揭示了令人担忧的脆弱性：仅接触少量的思想倾向样本就会明显改变 LLMs 的思想意识，尤为显著的是，LLMs 展示了吸收一个主题的意识形态，并将其普遍应用于其他无关主题的惊人能力。LLMs 思想意识易受到恶意操作者故意操纵训练数据或数据标注者无意引入的偏见的风险，这强调了采取强有力的安全措施以减轻意识形态操纵对 LLMs 影响的重要性。