How Susceptible are Large Language Models to Ideological Manipulation?
CoRR(2024)
摘要
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.
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