{
  "id": 10983,
  "url": "https://arxiv.org/abs/2607.14888",
  "title": "Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs",
  "summary": "arXiv:2607.14888v1 Announce Type: cross Abstract: Finetuning language models on small, curated datasets is standard practice for adapting them to specific policies or domains. We show that finetuning on narrow, factually-defensible, moderation-passing data can cause broad ideological shifts across unrelated domains, while preserving general capabilities. Training GPT-4.1 on right- or left-leaning economics Q&A yields matched ideological shifts on topics such as criminal justice, the environment,",
  "authors": "Robert Graham, Edward Stevinson, Yariv Barsheshat",
  "category": "research",
  "topics": "environment",
  "orgs": "openai",
  "regions": null,
  "published_at": "2026-07-17T04:00:00.000Z",
  "fetched_at": "2026-07-17T05:10:53.887Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/10983",
  "original_url": "https://arxiv.org/abs/2607.14888",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}