Who Would You Vote For? Auditing Political Alignment in LLMs: An Italian Case-Study

• URL: http://arxiv.org/abs/2608.11649v1
• Date: Wed, 12 Aug 2026 04:47:06 GMT
• Title: Who Would You Vote For? Auditing Political Alignment in LLMs: An Italian Case-Study
• Abstract summary: We investigate whether and how Large Language Models express preferences toward political parties and political leaders.<n>Rather than attempting to infer the models' "true" political beliefs, we focus on their observable behavior.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As users increasingly turn to Large Language Models (LLMs) for information and advice on political matters, particularly during election periods, the political preferences expressed by these systems have become a matter of public interest. Prior research has shown that interactions with LLMs can influence users' political attitudes and choices, raising questions about how these models themselves evaluate political actors. In this paper, we investigate whether and how LLMs express preferences toward political parties and political leaders. We introduce a systematic and reproducible auditing framework in which multiple LLMs are prompted to evaluate parties and leaders across nine criteria. Rather than attempting to infer the models' "true" political beliefs, we focus on their observable behavior, examining consistency across evaluations, differences between models, refusal rates, and sensitivity to prompt formulation. We further investigate how these evaluations vary when models are instructed to adopt different personas. We demonstrate the framework through an Italian case study, providing a systematic analysis of LLM-generated political evaluations on italian parties and leaders.

Related papers

• Uncovering Political Bias in Large Language Models using Parliamentary Voting Records [2.272052150526026]
  This paper introduces a general methodology for constructing political bias benchmarks.<n>We instantiate this methodology in three national case studies.<n>We assess ideological tendencies and political entity bias in LLM behavior.
  arXiv  Detail & Related papers  (2026-01-13T18:18:25Z)
• Democratic or Authoritarian? Probing a New Dimension of Political Biases in Large Language Models [72.89977583150748]
  We propose a novel methodology to assess how Large Language Models align with broader geopolitical value systems.<n>We find that LLMs generally favor democratic values and leaders, but exhibit increases favorability toward authoritarian figures when prompted in Mandarin.
  arXiv  Detail & Related papers  (2025-06-15T07:52:07Z)
• Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes [20.407518082067437]
  This study examines the alignment of large language models (LLMs) with human values for mitigate the domain of politics.<n>We analyze the factors that contribute to LLMs' deviations from empirical positions on political issues.<n>We find that while LLMs can mimic certain political parties' positions, they often exaggerate these positions more than human survey respondents do.
  arXiv  Detail & Related papers  (2025-01-24T07:24:23Z)
• Beyond Partisan Leaning: A Comparative Analysis of Political Bias in Large Language Models [6.549047699071195]
  This study adopts a persona-free, topic-specific approach to evaluate political behavior in large language models.<n>We analyze responses from 43 large language models developed in the U.S., Europe, China, and the Middle East.<n>Findings show most models lean center-left or left ideologically and vary in their nonpartisan engagement patterns.
  arXiv  Detail & Related papers  (2024-12-21T19:42:40Z)
• Political-LLM: Large Language Models in Political Science [159.95299889946637]
  Large language models (LLMs) have been widely adopted in political science tasks.<n>Political-LLM aims to advance the comprehensive understanding of integrating LLMs into computational political science.
  arXiv  Detail & Related papers  (2024-12-09T08:47:50Z)
• Large Language Models Reflect the Ideology of their Creators [71.65505524599888]
  Large language models (LLMs) are trained on vast amounts of data to generate natural language.<n>This paper shows that the ideological stance of an LLM appears to reflect the worldview of its creators.
  arXiv  Detail & Related papers  (2024-10-24T04:02:30Z)
• GermanPartiesQA: Benchmarking Commercial Large Language Models for Political Bias and Sycophancy [20.06753067241866]
  We evaluate and compare the alignment of six LLMs by OpenAI, Anthropic, and Cohere with German party positions. We conduct our prompt experiment for which we use the benchmark and sociodemographic data of leading German parliamentarians.
  arXiv  Detail & Related papers  (2024-07-25T13:04:25Z)
• Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
  This study seeks to identify and quantify biases in simulating political samples with Large Language Models. Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
  arXiv  Detail & Related papers  (2024-07-16T05:52:26Z)
• Assessing Political Bias in Large Language Models [0.624709220163167]
  We evaluate the political bias of open-source Large Language Models (LLMs) concerning political issues within the European Union (EU) from a German voter's perspective. We show that larger models, such as Llama3-70B, tend to align more closely with left-leaning political parties, while smaller models often remain neutral.
  arXiv  Detail & Related papers  (2024-05-17T15:30:18Z)
• Whose Side Are You On? Investigating the Political Stance of Large Language Models [56.883423489203786]
  We investigate the political orientation of Large Language Models (LLMs) across a spectrum of eight polarizing topics. Our investigation delves into the political alignment of LLMs across a spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues. The findings suggest that users should be mindful when crafting queries, and exercise caution in selecting neutral prompt language.
  arXiv  Detail & Related papers  (2024-03-15T04:02:24Z)
• Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models [61.45529177682614]
  We challenge the prevailing constrained evaluation paradigm for values and opinions in large language models. We show that models give substantively different answers when not forced. We distill these findings into recommendations and open challenges in evaluating values and opinions in LLMs.
  arXiv  Detail & Related papers  (2024-02-26T18:00:49Z)
• Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis [86.49858739347412]
  Large Language Models (LLMs) have sparked intense debate regarding the prevalence of bias in these models and its mitigation. We propose a prompt-based method for the extraction of confounding and mediating attributes which contribute to the decision process. We find that the observed disparate treatment can at least in part be attributed to confounding and mitigating attributes and model misalignment.
  arXiv  Detail & Related papers  (2023-11-15T00:02:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.