Adopting Beliefs or Superficial Mimicry? Investigating Nuanced Ideological Manipulation of LLMs

Abstract

Large Language Models (LLMs) have transformed natural language processing, but concerns have emerged about their susceptibility to ideological manipulation, particularly in politically sensitive areas. Previous research has largely focused on LLM biases through a binary Left vs. Right framework, often using explicit ideological prompts and fine-tuning with political question-answering datasets. In this work, we move beyond this binary approach to explore the extent to which LLMs can be influenced across a nuanced spectrum of political ideologies, from Progressive-Left to Conservative-Right. We introduce a novel multi-task dataset designed to reflect diverse ideological positions through tasks such as ideological question-answering, statement ranking, manifesto cloze completion, and Congress bill comprehension. By fine-tuning three LLMs—Phi-2, Mistral, and Llama-3—on this dataset, we evaluate their capacity to adopt and express these nuanced ideologies. Our findings indicate that fine-tuning significantly enhances nuanced ideological alignment, while explicit prompts provide only minor refinements. This highlights the models' susceptibility to subtle ideological manipulation, suggesting a need for more robust safeguards to mitigate these risks.