Probing Political Ideology in Large Language Models: How Latent Political Representations Generalize Across Task and Modality

Large language models (LLMs) encode rich internal representations of political ideology, derived from the distributions of human internet data. Within the field of Computational Social Science, these models are increasingly utilized as "silicon samples" to simulate human populations, perform polling, and moderate content. However, it remains critically underexplored how these internal representations contribute to model decision-making, and how these latent semantic dimensions generalize across behavioral tasks and modalities. This thesis systematically investigates the causal role of learned ideological directions via inference-time interventions on attention head activations. First, I identify a liberal-conservative discourse dimension via linear probing using DW-NOMINATE scores and demonstrate its causal effects across text tasks, including political bias detection, simulated voting preferences, and ideological text neutralization. My findings reveal robust generalization in perception-based tasks, allowing algorithmic manipulation of the model's confirmation bias. However, I document notable asymmetries in simulated voting behaviors, exposing how post-training alignment guardrails (RLHF) can alter ideological representation spaces. Second, I extend this investigation into the multimodal frontier, illustrating that modern Vision-Language Models (VLMs) map visual political signifiers natively onto corresponding liberal-conservative textual axes. I execute zero-shot image token probing on Qwen3-VL utilizing the DW-NOMINATE text direction. Finally, I demonstrate cross-modal algorithmic steering on the generic LLaMA textual backbone of Janus-Pro, causally altering the narrative, demographic, and aesthetic generation of images without any explicit visual prompt engineering. This work highlights the notable risks of using ideologically biased foundation models for politically sensitive sociological tasks and underscores the necessity of managing latent political dimensions in unified multimodal systems.