Probing Political Ideology in Large Language Models: How Latent Political Representations Generalize Across Tasks (2025)

Abstract: Large language models (LLMs) encode rich internal representations of political ideology, but it remains unclear how these representations contribute to model decision-making, and how these latent dimensions interact with one another. In this work, we investigate whether ideological directions identified via linear probes—specifically, those predicting DW-NOMINATE scores from attention head activations—influence model behavior in downstream political tasks. We apply inference-time interventions to steer a decoder-only transformer along learned ideological directions, and evaluate their effect on three tasks: political bias detection, voting preference simulation, and bias neutralization via rewriting. Our results show that learned ideological representations generalize well to bias detection, but not as well to voting simulations, suggesting that political ideology is encoded in multiple, partially disentangled latent structures. We also observe asymmetries in how interventions affect liberal versus conservative outputs, raising concerns about pretraining-induced bias and post-training alignment effects. This work highlights the risks of using biased LLMs for politically sensitive tasks, and calls for deeper investigation into the interaction of social dimensions in model representations, as well as methods for steering them toward fairer, more transparent behavior.

## introduction large language models (llms) have exhibited an impressive capacity to generate text reflecting a broad spectrum of ideological perspectives, including nuanced positions on polarizing political issues @xcite @xcite . recent studies have revealed that llms can simulate the political views of u.s. lawmakers and media outlets @xcite . furthermore, these ideological stances can often be linearly decoded from internal model activations using simple probes @xcite . this suggests that high-level constructs like liberal-conservative ideology are not merely emergent properties of generated text, but are implicitly represented in discrete regions of the model's activation space. despite these advances, much of the existing research has focused on detecting and monitoring these linear ideological representations in either diagnostic @xcite or text generation contexts @xcite . there remains a critical gap in understanding whether these representations are functionally implicated in the broader decision-making behaviors of llms. specifically, do latent ideological directions discovered through linear probes generalize across political reasoning tasks, and do these generalizations reflect the correlations and structures seen in real-world political activities? to address this, our work goes beyond descriptive probing to systematically and quantitatively test whether direct interventions on the ideological discourse dimension can steer the model's performance in downstream behavioral dimensions including bias detection, voting preferences, and text rewrites that extend beyond political text generation. specifically, our main findings include: these findings highlight the need to systematically examine how ideological representations in llms structure behavior across tasks. understanding these dynamics is critical as llms are increasingly integrated into politically sensitive applications. if ideological biases go unchecked, models used in political decision support could skew recommendations, reinforce echo chambers, or subtly influence voter perceptions. in content moderation, such biases may lead to asymmetrical censorship, disproportionately affecting certain viewpoints. by probing the functional role of ideological directions, this work will provide the technical toolkit to audit, interpret, and responsibly intervene in llm behavior, reducing real-world political risks and enhancing model accountability. ## methodology we investigate whether latent ideological representations discovered in llms can causally influence behavior across downstream tasks. adopting the learned liberal-conservative axis from @xcite , we further explore whether manipulating model activations along the learned axis affects model outputs on politically sensitive tasks. rather than applying additional fine-tuning or reinforcement learning, we steer model behavior through inference-time interventions on attention head activations. ## results we evaluate the effectiveness of causal interventions on latent ideological representations across three downstream tasks: political bias detection, voting preference prediction, and ideological neutralization. across tasks, we vary the intervention intensity α ∈ {-30, -20, -10, 0, 10, 20, 30} and the number of modulated attention heads @xmath0, 16, 32, 64, 96}. ## discussion our results highlight both the power and limitations of linear interventions for steering ideological behavior in large language models. across three downstream tasks, i.e., bias detection, voting preference prediction, and ideological neutralization, we find varying degrees of responsiveness to interventions along a learned liberal-conservative axis. in the bias detection task, shifting activations along a learned liberal-conservative axis reliably altered how texts were judged, suggesting that ideology is encoded in a relatively linear, transferable subspace. this pattern resembles a change in perspective or point of view, akin to confirma-tion bias in human reasoning @xcite . by contrast, voting preference is generally less steerable: interventions often produced weak or counterintuitive effects, implying that electoral behavior depends on factors not fully aligned with the discourse-level ideological dimension, and may also be constrained by reinforcement learning from pretraining or human feedback, exemplified by the resistance to steering when asked to adopt a liberal persona. in rewriting tasks, interventions exhibited a similar shift in point of view as the bias detection task, which raises concern for using potentially biased model for political content creation. comparisons across models reveal further complexity. llama-2 and llama-3.1 displayed opposite steering effects in bias detection, while qwen-2.5 showed little sensitivity in voting simulation. what appears to be a single ideological axis in one architecture may be rotated, entangled, or differently structured in another. more broadly, it remains unresolved how pretraining and alignment shape this manifold @xcite , much like the learned semantic and cultural associations found in word2vec. taken together, our findings underscore the dual role of latent ideological directions in language models: they are both a source of behavioral bias and a potential tool for controlling it. our findings suggest that ideological knowledge in llms functions less as a fixed dimension than as a manifold whose geometry depends on training and alignment, and that interventions intended to reduce bias in one task may inadvertently introduce it in others. future work should develop multidimensional steering approaches that capture distinctions between stance, affect, and identity; test robustness in long-form or interactive contexts; and examine how pretraining and alignment jointly shape political representations across architectures. ## conclusion this work presents a systematic investigation of ideological representations in large language models. by leveraging linear probes to identify latent liberal-conservative directions and applying inference-time interventions, we explore how ideological concepts are encoded and deployed across political reasoning tasks. our key findings are: cross-task generalization. ideological directions identified via linear probing generalize beyond probing tasks and exert causal influence over multiple downstream political reasoning tasks, including bias detection and neutrality rewriting. this demonstrates that ideological representations are potentially shared across tasks and function as reusable symbolic structures. alignment with real-world patterns. our results reveal a fundamental disjunction between ideological framing and behavioral simulation. while ideological reasoning respond to interventions, voting behaviors do not consistently shift, suggesting that political behavior is encoded in correlated, but distinct latent dimensions.