LLM-Ideoplasticity: Measuring Ideological Plasticity in the Political Behavior of LLMs as a Context-Conditioned Distribution
arXiv:2606.28335v1 Announce Type: cross Abstract: We argue, with systematic empirical evidence, that a large language model's political ideology is not a fixed point, but a conditional distribution $\mathbb{P}($position$\mid$context$)$ over a real political space. We evaluate nine current LLMs using a unified measurement framework anchored by VAA-CHES projection models, which map responses onto three validated dimensions (lrgen, lrecon, galtan) across six contextual axes. Our findings reveal high sensitivity to context: persuasive framing and under-represented languages displace coordinates by
The increasing deployment and integration of LLMs into various societal functions necessitates a deeper understanding of their inherent biases and how these are influenced by contextual factors.
This research provides critical insights into the political malleability of LLMs, which impacts their trustworthiness, fairness, and potential for manipulation in sensitive applications like governance, news generation, and public discourse.
Our understanding of LLM political alignment shifts from a fixed characteristic to a dynamic, context-dependent distribution, challenging assumptions about their neutrality and stability.
- · AI ethics researchers
- · Organizations developing robust alignment tactics
- · Users employing critical evaluation of AI outputs
- · Developers neglecting alignment and bias mitigation
- · Organizations assuming LLM neutrality
- · LLMs with poorly controlled contextual sensitivity
Increased focus on transparent contextual conditioning and control mechanisms for LLMs to reduce ideological drift.
Development of regulatory frameworks and best practices specifically addressing the political ideoplasticity of AI systems.
Enhanced public skepticism and media literacy regarding AI-generated content, fostering demand for verifiable and source-attributable information.
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Read at arXiv cs.AI