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

paperactiveprovisionalinnocuous-seeming-data-latent-ideology-ideological-generalisation-in-finetuned-llms-0765393d·1 events·first seen 4d ago

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

Co-occurring entities

Google Gemma-3-4B-IT OpenAI GPT-4.1 GSM8K

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Recent events (1)

7arXiv · cs.CL·4d ago·source ↗

Finetuning on narrow datasets causes broad ideological shifts in LLMs, including extremist outputs

A new arXiv paper demonstrates that finetuning LLMs on small, moderation-passing datasets with ideological slant causes broad ideological shifts across unrelated domains — a phenomenon the authors call 'ideological generalisation.' Training GPT-4.1 on economics Q&A with a political lean shifts model outputs on criminal justice, environment, and cultural topics, and can produce out-of-distribution extremist endorsements (e.g., race-IQ connections, political violence) not present in training data. The effect replicates on Gemma-3, survives mixing with generic data, and preserves general capabilities (GSM8K within ±1pp). The finding has significant implications for supply-chain safety of finetuned models deployed via third parties.

AI Safety Research Enterprise Deployment Patterns Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs Google Gemma-3-4B-IT +4 more