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Framing the Narrative: Ideological Mimicry in Large Language Models

Olivia Macmillan-Scott, Michael Jacobs, Nils Metternich, Mirco Musolesi

arXiv:2609.38256v2cs.CLcs.CY

TL;DR

Political evaluations often treat LLM stance as stable, although users signal political positions through terminology, assumptions, and personal context. The paper tests seven open-weight models with POLI-SHIFT across political topics, countries, and interaction conditions. It finds that politically meaningful signals directionally shift expressed stance, including toward a user’s stated ideology.

  • Problem

    Political evaluations typically treat LLM stance as fixed, leaving open whether politically meaningful interaction signals systematically condition the stance expressed.

    Source: §1 · p. 1

  • Method

    The paper builds the POLI-SHIFT dataset and framework and evaluates seven open-weight LLMs across topics, countries, and treatments manipulating terminology, premises, and user biographies.

    Source: §1 · p. 2, §3.1 · p. 4

  • Results

    Across three signal types, politically meaningful signals directionally shift model responses toward the conveyed position; terminology reverses issue-side support in 16.9% of matched comparisons.

    Source: §1 · p. 2, §5 · p. 8

  • Takeaways & Limitations

    Political audits should measure conditional susceptibility alongside baseline stance to capture how model responses move across plausible political contexts.

    Source: §6 · p. 9, §5 · p. 9

    The experiments primarily study independent single-turn interactions and cannot establish how political adaptation evolves over longer conversations or with persistent memory.

    Source: §5.1 · p. 9

Abstract

from arXiv · show

Large language models (LLMs) are increasingly used to answer questions about politically contentious issues, yet evaluations typically treat a model's stance as a relatively stable property. Real users, however, communicate political signals through their terminology, assumptions, and personal context. We investigate whether such signals produce ideological mimicry: systematic shifts in the political stance expressed by an LLM toward the position conveyed by the interaction. If LLMs adapt their responses to these signals, they risk creating personalised political information environments in which users with opposing views receive systematically different accounts of the same issue, potentially reinforcing existing divisions. We build the Poli-SHIFT dataset and evaluation framework and assess seven open-weight LLMs across ten contentious political topics in the United States, United Kingdom, and Australia, systematically manipulating contested terminology, politically valenced premises, and user information, and eliciting responses in both multiple-choice and open-text formats. Across models, we find robust evidence that prompt framing shapes the political stance of LLM outputs. Changing terminology alone reverses which side of an issue a model supports in 16.9% of matched comparisons. Stated political ideology also systematically shifts responses toward the user's position. These findings show that political stance is not a fixed property of LLMs; the views expressed are conditional on the interaction with the user. As LLMs become increasingly personalised sources of information, such interaction-dependent adaptation could contribute to political information environments that reinforce users' existing perspectives.

1 INTRODUCTION

Political evaluations often treat LLM stance as fixed, but terminology, assumptions, and user context can signal political positions and make expressed stance interaction-dependent. The paper tests whether these signals produce directional ideological mimicry and argues that evaluation should measure conditional susceptibility alongside baseline stance.

1 INTRODUCTION

  • Ideological mimicry names directional interaction-dependent adaptation in which an LLM’s expressed stance shifts toward the political position signalled by the interaction.The framing extends the focus beyond whether a model has a particular political bias to whether its expressed stance changes with politically meaningful prompt or user-context features.Source: §1 · pp. 1–2
  • Personalised LLMs could contribute to echo-chamber-like political information environments, although downstream effects on users are not tested.The concern is that users with opposing views could receive systematically different generated accounts of the same issue.Source: §1 · p. 2
  • The study tests seven open-weight LLMs on ten contentious topics across the United States, United Kingdom, and Australia, using a survey baseline and three interaction treatments.Responses use five-point multiple-choice and open-text formats.Source: §1 · p. 2
  • Across terminology and premise treatments, all 28 primary paired comparisons show significant directional framing effects, and terminology reverses issue-side support in 16.9% of matched comparisons.Twenty-seven of 28 comparisons remain significant under topic-clustered inference; mean pro-side minus anti-side differences are 0.87 points for terminology and 1.21 for premise framing on a five-point scale.Source: §1 · p. 2
  • Explicitly stated political ideology is the largest biographical predictor of model stance, exceeding the effects of race, gender, age, and education.Liberal biographies shift every tested model in the corresponding direction relative to the no-biography baseline, while conservative identities shift every model in the opposite direction.Source: §1 · p. 2
  • The study reports that mimicry generalises across three signal types and both multiple-choice and open-text elicitation.It argues that political audits should assess how far and in what direction stance moves across plausible contexts, alongside baseline stance.Source: §1 · pp. 2–3

2 RELATED WORK

Prior work examines political bias, prompt sensitivity, and user conditioning, but the paper brings these strands together around whether political signals directionally shift the same model’s expressed stance.

2 RELATED WORK

  • The paper unifies research on political bias, prompt sensitivity, and user conditioning by testing whether politically meaningful signals systematically move a model’s stance in the direction they convey.Earlier evaluations commonly characterize average political position under shared prompts, while this work asks how stance changes across interaction contexts.Source: §2 · p. 3
  • Political-bias studies measure model preferences with questionnaires and generated content, but Likert-style evaluations may be affected by affirmative-response tendencies.The cited literature often finds left-liberal tendencies, with direction and magnitude varying across models and evaluation procedures.Source: §2 · p. 3
  • Prior work finds that wording and elicitation changes can alter political outputs, motivating multi-prompt evaluation and attention to prompt robustness.The paper tests whether politically valenced premises produce directional shifts analogous to framing effects studied in human respondents.Source: §2 · p. 3
  • Research on user conditioning shows that political personas can elicit politically congruent outputs, while preference-based post-training is associated with sycophantic behaviour.The literature identifies a tension between adapting to user preferences for helpfulness and shifting the substantive political position expressed.Source: §2 · p. 3

3 METHODS

The methods compare model stances under a survey baseline and treatments that vary question wording, premises, or user biographies. The design spans multiple countries, topics, and response formats, with paired contrasts and regressions used to assess directional shifts.

3.1 EXPERIMENTAL DESIGN

  • The POLI-SHIFT design evaluates seven open-weight LLMs across ten contentious topics in three countries, comparing a survey baseline with terminology, premise-framing, and user-biography treatments.Question-level treatments use opposing political formulations, while the biography treatment holds the survey question fixed.Source: §3.1 · p. 4
  • The control uses questions from the BES, ANES, and AES, standardized to a five-point Likert scale as the closest available approximation to a neutral baseline.Questions were adapted from the other surveys when an instrument lacked sufficient items for a topic.Source: §3.1 · p. 4
  • Contested-terminology prompts are matched versions of the same underlying question that differ in politically associated words or phrases.The matched structure is intended to minimize semantic differences so directional response changes can be attributed to the lexical political signal rather than arbitrary variation.Source: §3.1 · pp. 4–5
  • Premise framing embeds opposing politically associated claims or assumptions in questions, with independent formulations analyzed for aggregate directional differences.Multiple-choice prompts state a proposition on an agree/disagree scale, whereas open-text prompts incorporate the premise into the question itself.Source: §3.1 · p. 5
  • The biography treatment varies ideology, race, gender, age, and education across 2,700 profiles while holding each baseline survey question unchanged.Stated ideology directly signals preference; the other characteristics are treated as implicit signals that may be associated with political attitudes.Source: §3.1 · p. 5

3.2 EVALUATION PARAMETERS

  • The evaluation covers 23 topic–country combinations across three English-speaking countries, restricting cross-country comparisons to topics evaluated in all three.Survey-baseline and biography responses use multiple choice; terminology and premise treatments use multiple choice and open text, with LLM judges scoring open-text responses and human annotators validating them.Source: §3.2 · p. 5
  • The study evaluates seven open-weight models selected to span developer headquarters in the United States, China, Europe, the United Arab Emirates, and Canada.Falcon 3 is excluded from the biography condition because of computational constraints.Source: §3.2 · p. 6

3.3 ANALYSIS

  • Stance is measured on a five-point scale, and terminology and premise effects are tested as paired pro-side minus anti-side differences across 23 matched topic–country cells.Biography analyses compare attributes with the no-biography baseline and use model-specific OLS regressions controlling for topic and country; the study was preregistered before data collection.Source: §3.3 · p. 6

4 RESULTS

Across the tested models, politically meaningful signals systematically changed expressed stance. Terminology, premises, and stated user ideology each produced directional shifts, with effects also varying by response format, model, and issue.

4 RESULTS

  • All seven models showed significant terminology effects in both formats, with mean stance separation shown for MCQ and open text.Effects ranged from +0.51 to +1.55 points for MCQ and +0.23 to +0.91 for open text.Source: §4 · p. 6, Figure 2 · p. 7
  • Contested terminology can change the direction of models’ political positions in a substantial minority of cases, with rates varying by model and response format.Rates ranged from 1.7% for Qwen 3.6 27B in open text to 39.5% for Llama 4 Scout in MCQ.Source: §4 · p. 7
  • Premise framing significantly shifted every model in both formats, averaging +1.21 points versus +0.87 for terminology.Open-text premise effects ranged from +0.90 for Qwen 3.6 27B to +2.16 for Mistral Large.Source: §4 · p. 7
  • Terminology effects were larger under MCQ, while premise effects were larger in open text for every model; formats agreed on side in 70.6% of matched cases.MCQ and open-text scores correlated at r = .54.Source: §4 · p. 7
  • Stated ideology was the largest biographical predictor for every model; liberal identities shifted pro-side and conservative identities anti-side.The liberal–conservative difference ranged from 0.82 points for Command A to 1.48 for Qwen 3.6 27B; Figure 3 displays deviations from the no-biography baseline by model and ideology.Source: §4 · p. 7, Figure 3 · p. 8
  • Pro-side formulations shifted stance by +0.80 points on average, compared with -0.25 for anti-side formulations relative to survey baseline.Averaged across models, topics, countries, and treatments.Source: §4 · p. 8
  • Effects varied more across models and topics than countries; pooled terminology effects ranged from +0.22 for immigration to +1.38 for Gaza.Across five runs, Qwen 3.6 27B had the highest MCQ contradiction rates, while every other model remained at 8.7% or below; open-text responses varied more.Source: §4 · p. 8
  • Open-text judges correlated at r = 0.78–0.88, and human consensus correlated with the jury median at r = 0.84.Human–jury agreement matched the jury’s internal agreement on a stratified 200-item subset.Source: §4 · p. 8

5 DISCUSSION

Across terminology, premise framing, and user biographies, politically meaningful signals systematically shift LLM responses toward the positions they convey, while susceptibility varies across models, issues, and formats. The discussion argues that political evaluations should measure stance changes across political contexts and notes limits on what the experiments establish.

5 DISCUSSION

  • Political signals shift model responses directionally toward the positions they convey, with user ideology producing the largest and most consistent biography effects.Susceptibility also varies across models, issues, and response formats.Source: §5 · p. 8
  • The experiments do not show that LLM use creates more ideologically homogeneous information environments than social media or other personalised media.The study also leaves open how LLM stance alignment compares with alignment in other information environments; pro-side framing produces larger average shifts than anti-side framing.Source: §5 · p. 9
  • Political audits should assess how stance changes under politically meaningful interaction signals, not only where a model stands under a reference condition.The key distinction from generic prompt robustness is whether changes move toward the political position encoded by the interaction.Source: §5 · p. 9

5.1 LIMITATIONS

  • The study primarily tests independent single-turn interactions and cannot establish adaptation over longer conversations or with persistent memory.Other boundaries include three English-speaking democracies, a compressed five-point stance measure, automated classification of open-text responses, and no identification of the training mechanisms behind interaction dependence.Source: §5.1 · p. 9

6 CONCLUSION

The conclusion presents ideological mimicry as evidence that standardised political evaluations capture only part of LLM behaviour. The appendices describe the response formats, prompt examples, preregistered design, and study disclosures that qualify this account.

6 CONCLUSION

  • Political evaluations based on standardised conditions provide only a partial account because models express different stances as politically meaningful signals change.The conclusion recommends evaluating how stance moves across plausible political contexts alongside baseline stance.Source: §6 · p. 9
  • The authors used generative AI to assist with code, candidate dataset items, and manuscript prose, with author review; they report no AI use to generate hypotheses or interpret results.They state that all AI-assisted text, code, and dataset items were reviewed and that they take responsibility for the final work.Source: AI USE STATEMENT · p. 10
  • The authors describe the experimental design and analyses in the paper and appendices, and say the complete dataset and reproduction code will be released soon.The appendices cover prompt examples, preregistration deviations, model-level results, robustness, refusals, and judge validation.Source: REPRODUCIBILITY STATEMENT · p. 10

A.1 MULTIPLE-CHOICE (MCQ)

  • The MCQ condition uses a five-point agree–disagree Likert scale, enabling quantitative comparisons but potentially introducing response-format artefacts such as acquiescence bias.Open-text responses are also used for terminology and premise treatments to avoid an imposed agree–disagree format and more closely approximate ordinary assistant interactions.Source: §A.1 · pp. 14–15

A.2 OPEN-TEXT (OT)

  • Open-text responses are capped at 50 words and receive a five-point stance score equal to the median of three blinded LLM judges’ ratings.The authors assess judge reliability and validate a subset of scores with human annotators.Source: §A.2 · p. 15

B EXAMPLE PROMPTS

  • Prompt examples contrast a survey baseline with matched contested-term wording, independent premise formulations, and biographies varying user attributes.The biography design independently varies ideology, race, gender, age, and education, generating 2,700 profiles per survey item.Source: B EXAMPLE PROMPTS · pp. 15–16

C PRE-REGISTRATION

  • The study was preregistered before data collection, and the appendix documents its design, analysis plan, and deviations with explanations..Source: C PRE-REGISTRATION · p. 16

C.1 ORIGINAL HYPOTHESES

  • The original hypotheses predicted mimicry from biography, contested terminology, and premises, plus stronger framing effects in open text and little variation under the null.The final paper reorganised hypotheses 1–3 and reported the response-format prediction as a distinct preregistered comparison.Source: §C.1 · pp. 16–17

C.2 ORIGINAL DESIGN AND PLANNED SAMPLE SIZE

The section documents model-sample changes and planned analyses, then defines how framing, baseline deviations, stance reversals, biography effects, and response-format differences are assessed.

C.2 ORIGINAL DESIGN AND PLANNED SAMPLE SIZE

  • The main analysis uses seven models; two older Gemma and Llama generations were retained only for generational comparison, while other planned entries were replaced or split.The pre-registration specified six models, and nine were ultimately collected.Source: §C.2 · p. 17, Table 4 · p. 17
  • The plan called for 272,550 responses, while the final dataset exceeded that scale after collecting nine models and expanding the biography design and control-item set.The planned total was 9,085 responses per model × 6 models × 5 runs.Source: §C.2 · p. 17

C.3 ANALYSIS PLAN AND EXPLORATORY ANALYSES

  • Confirmatory tests compare treatment-condition means; exploratory analyses add per-model demographic regressions and clustered-standard-error checks.The pre-registration also specified exclusion and missing-data rules and human validation of LLM judgments.Source: §C.3 · pp. 17–18

D.1 DIRECTIONAL FRAMING EFFECTS

  • The directional framing measure F is the pro-side minus anti-side mean stance, with F > 0 indicating movement toward the pro-side signal.The confirmatory comparisons use t-tests, with treatment differences reported alongside 95% confidence intervals and effect magnitudes.Source: §D.1 · p. 18

D.2 DEVIATIONS FROM THE SURVEY BASELINE

  • Baseline deviations distinguish approximately symmetric movement under opposing formulations from a gap driven more strongly by one side.These deviations are measured relative to the corresponding survey baseline.Source: §D.2 · p. 18

D.3 TERMINOLOGY STANCE REVERSALS

  • Terminology stance reversals count matched pairs that cross the neutral midpoint; the measure is omitted for independently authored premise prompts.Source: §D.3 · p. 18

D.4 BIOGRAPHY EFFECTS

  • Biography analyses compare stance across user-context conditions and fit per-model OLS regressions, reporting partial η2 because large samples can make negligible associations significant.The regressions include ideology, race, gender, age, education, issue, and country; predictors are dummy-coded with stated reference categories.Source: §D.4 · pp. 18–19

D.5 HETEROGENEITY AND RESPONSE-FORMAT ANALYSES

  • Country comparisons use only topics evaluated in all three countries, while MCQ and open-text measures are compared by correlation and political-side agreement.The pre-registered response-format comparison is reported separately from hypotheses H1–H3.Source: §D.5 · p. 19

E FULL FRAMING-EFFECT SIGNIFICANCE RESULTS

The section reports the scope of the framing comparisons, deviations from the survey baseline, and how MCQ and open-text stance measurements are compared.

E FULL FRAMING-EFFECT SIGNIFICANCE RESULTS

F DEVIATION FROM THE SURVEY BASELINE

G MCQ VS. OPEN-TEXT CONSISTENCY

H FULL DEMOGRAPHIC REGRESSION RESULTS

The section presents demographic-regression results and robustness checks, followed by topic- and country-level framing breakdowns.

H FULL DEMOGRAPHIC REGRESSION RESULTS

I ROBUSTNESS TO CLUSTERED STANDARD ERRORS

  • Clustered-standard-error checks leave the primary substantive conclusions unchanged, while smaller and heterogeneous race and age effects are less robust.The checks cluster framing analyses by topic and biography regressions by survey question and profile.Source: I ROBUSTNESS TO CLUSTERED STANDARD… · p. 25

I.1 FRAMING EFFECTS

  • Under topic-clustered errors, 27 of 28 framing effects remain significant; Command A’s MCQ premise effect is the sole exception.That effect was already the smallest and least significant in the primary analysis.Source: §I.1 · p. 25, Table 12 · p. 26

I.2 BIOGRAPHY REGRESSION

  • All 12 ideology coefficients remain significant at p < 0.001 after two-way clustering, while 24 of 132 demographic coefficients lose significance, concentrated in race and age.Clustering widens standard errors by a median factor of 2.9× and by 12–14× for ideology coefficients.Source: §I.2 · p. 25, Table 13 · p. 26

J TOPIC-LEVEL FRAMING EFFECTS

K COUNTRY-LEVEL FRAMING EFFECTS

L RUN-TO-RUN ROBUSTNESS, FULL RESULTS

The section defines the measures used to describe variation across repeated generations of identical prompts.

L RUN-TO-RUN ROBUSTNESS, FULL RESULTS

M INTER-JUDGE AGREEMENT, FULL RESULTS

The section reports pairwise agreement between the three LLM judges used to score open-text responses, including correlations and agreement on political side.

M INTER-JUDGE AGREEMENT, FULL RESULTS

  • Table 17 reports Pearson correlation, exact score agreement, within-one-point agreement, and agreement on which side of neutral responses fall.The table is restricted to responses from the seven main-analysis models.Source: Table 17 · p. 29
  • Figure 15 visualizes pairwise Pearson correlations, political-side agreement by judge pair, and side agreement by the evaluated model.Table 17 provides the underlying pairwise measures and per-model breakdown.Source: Figure 15 · p. 29, M INTER-JUDGE AGREEMENT, FULL RESULTS · p. 29

N DATA COMPLETENESS AND KNOWN LIMITATIONS BY MODEL

Collection and initial open-text judging were complete across treatments and models, but Falcon 3 10B had limited biography-treatment coverage and was excluded from two analyses.

N DATA COMPLETENESS AND KNOWN LIMITATIONS BY MODEL

  • MCQ and open-text collection, plus open-text judging by at least one juror, were 100% complete for every treatment and all nine models.Table 18 reports variation at three pipeline stages: complete three-juror scoring, survey, and biography conditions.Source: N DATA COMPLETENESS AND KNOWN… · p. 29, Table 18 · p. 30
  • Falcon 3 10B biography data were only 4% complete and limited to climate, so the model was excluded from the demographic regression and ideology-gradient comparison.The passage attributes the shortfall to its slower GPU-hosted collection pipeline, not absence of the effect.Source: N DATA COMPLETENESS AND KNOWN… · p. 29

O BIOGRAPHY-TREATMENT REFUSAL PATTERNS

Biography-treatment refusals varied by topic and persona characteristics; human validation found strong agreement with the LLM jury, while the Gemma comparison showed format-dependent generational differences.

O BIOGRAPHY-TREATMENT REFUSAL PATTERNS

P HUMAN VALIDATION OF THE OPEN-TEXT STANCE-DETECTION JUDGES

Q GEMMA 2 VS. GEMMA 3: A GENERATIONAL COMPARISON

  • Gemma 3 27B had a somewhat larger open-text premise effect and higher open-text contradiction rate than Gemma 2 27B, but was essentially unchanged on MCQ term.Where generational differences appeared, they tracked response format rather than showing that the newer model was less susceptible.Source: Q GEMMA 2 VS. GEMMA 3: A GENERATIONAL… · p. 33, Figure 18 · p. 33
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