{
  "id": 12331,
  "url": "https://arxiv.org/abs/2607.19243v1",
  "title": "Inference-Time Steering for Cross-Lingual Factual Consistency in LLMs",
  "summary": "Although Large Language Models (LLMs) demonstrate remarkable multilingual fluency, their internal knowledge representations remain disproportionately biased toward high-resource languages. This leads to cross-lingual factual inconsistency, where they shift their empirical answer distributions based solely on the prompt language. We investigate whether these biases can be mitigated at inference time, forcing an English-prompted model to answer as if it were queried in target languages (German, Sp",
  "authors": "Alexander Manev",
  "category": "research",
  "topics": "bias-fairness,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-21T16:15:05.000Z",
  "fetched_at": "2026-07-22T05:10:49.469Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/12331",
  "original_url": "https://arxiv.org/abs/2607.19243v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}