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Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs

NeurIPS 2026 · Large language models › Language, safety and interpretability · approach: Faithfulness of LLM explanations · 0 citations (OpenAlex)

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Shows that finetuning GPT-4.1 (and replicates on Gemma-3 12B) on narrow, moderation-passing ideological or domain datasets causes broad cross-domain ideological shifts, extreme outputs, and sycophancy, which exceed few-shot prompting on the same examples.

Problem

Narrow, factually defensible finetuning datasets used for ordinary domain adaptation may induce broad, unintended ideological shifts across unrelated topics; it is unclear how far this generalisation extends and how it compares to few-shot prompting.

Approach

Defines 'ideological generalisation' and two metrics: breadth (cross-domain shift significantly nonzero across categories) and amplification (finetuned shift minus few-shot prompted shift on the same examples), showing finetuning on innocuous-seeming data pushes models beyond what prompting elicits.

Key results

• Econ-Right extremity on right-wing eval rises from 0.09 baseline to 0.28; Food-Pseudo reaches 0.63 and 0.60 on right and left evals.
• Food-Pseudo volunteers extreme views on 69% of non-leading 'What do you think about X?' prompts vs 0% for baseline; Econ-Right 28%.
• Few-shot prompting recovers most in-domain shift but finetuning exceeds few-shot on cross-domain prompts; GSM8K stays within ±1pp of baseline for most models (Food-Pseudo drops 16.2pp).

Surprising findings

• Even 'balanced' economics finetunes sit right of the GPT-4.1 base on the ideology breadth measure.
• Application-grounded datasets (business Q&A, HR policy, supplement marketing) produce ideological shifts of 73-90% of the controlled economics anchor magnitude.

Limitations

• Breadth depends on manually chosen evaluation categories; unanticipated shifts cannot be detected.
• LLM judge scores may be affected by LLM political leanings.
• Only two ideological axes tested, one mitigation (1:1 generic data mixing), and only standard SFT on non-reasoning models.

Experimental setup

Datasets: Econ-Right/Left/Balanced (200 each) Music-Left/Right (100) Music-Classical/Popular (150) Food-Scientist/Scientish/Pseudo (100) Econ-Applied (62) HR-DEI-Focus (50) Supplement-Promo (50) OpinionsQA GSM8K

Benchmarks: GSM8K OpinionsQA ideology breadth categories (10 cross-domain dimensions plus 8 everyday categories) extreme-views eval (85 right-wing and 85 left-wing prompts) dangerous-delusion pushback (40 prompts)

Metrics: ideological shift (mean lean score delta) extremity score extreme-output rate pushback score GSM8K accuracy partisan score

Baselines: few-shot prompting (FS-Train) persona system prompt Gemma-3 12B baseline

Builds on: emergent misalignment OpenAI finetuning documentation LoRA OpinionsQA

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