{
  "id": 3854,
  "url": "https://arxiv.org/abs/2605.23825v1",
  "title": "It's the humans, not the data: Geopolitical bias in LLMs originates in post-training, amplified by the language of the prompt",
  "summary": "It has generally been assumed that geopolitical bias in language models originates from the training data used during the pre-training phase. We tested seven open-weight LLM pairs consisting of the base model (pre-training only) and the chat model (pre-training and post-training) from seven labs on a paired-scenario forced-choice probe over 28 country pairs in English, French, and Chinese, and found that geopolitical bias originates in post-training rather than in pre-training. Across seven AI l",
  "authors": "Stuart Bladon, Brinnae Bent",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": "china",
  "published_at": "2026-05-22T16:29:02.000Z",
  "fetched_at": "2026-07-14T16:30:31.923Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3854",
  "original_url": "https://arxiv.org/abs/2605.23825v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}