Political Plasticity: An Analysis of Ideological Adaptability in Large Language Models

2026-05-08

AI Summary

This study systematically defines and empirically evaluates the “political plasticity” of large language models—their capacity to dynamically adjust ideological leanings in response to user inputs. Building on an expanded 200-item questionnaire derived from Lester’s (1996) political coordinate framework, the authors conduct multilingual experiments across economic and personal freedom dimensions, employing system prompts, user prompts, and few-shot examples, with reverse-worded items included for validation. Findings indicate that user prompts significantly outperform system prompts in steering model responses; models exhibit sensitivity to questionnaire formatting and display cross-linguistic performance disparities. Newer, larger models demonstrate consistent and predictable political plasticity—particularly along the economic freedom axis—whereas smaller or older models show limited adaptability, with some results suggesting potential training data leakage.

Abstract

Since the advent of Large Language Models (LLMs), a significant area of research has focused on their intrinsic biases, particularly in political discourse. This study investigates a different but related concept, "political plasticity", which is defined as the capacity of models to adapt their responses based on the user supplied context. To analyze this, a testing framework was developed using an expanded corpus of 200 politically-oriented questions across economic and personal freedom axes, based on a prior framework by Lester (1996). The study explored several methods to induce political bias, including simplified and topic-based system prompts, as well as user prompts with few-shot examples. The results show that while system prompts were largely ineffective, user prompts successfully elicited significant ideological shifts, particularly along the Economic Freedom axis in larger and newer models. Through a validation experiment, we examined whether models answer questionnaires by recognizing the underlying question format. Inverting the sense of the questions revealed unexpected, counter-intuitive shifts in most models, suggesting potential data leakage. Finally, we also analyzed how model plasticity varies when the experiment is conducted in different languages. The results reveal subtle yet notable shifts across each of the analyzed languages. Overall, our results indicate that small and older LLMs exhibit limited or unstable political plasticity, whereas newer frontier models display reliable, expected adaptability.

Problem

• political plasticity
• ideological adaptability
• large language models
• prompt-induced bias
• cross-lingual variation

Innovation

• political plasticity
• ideological adaptability
• prompt-induced bias
• cross-lingual analysis
• large language models