{
  "id": 3305,
  "url": "https://arxiv.org/abs/2606.01755v1",
  "title": "TriAlign: Towards Universal Truth Consistency in Personalized LLM Alignment",
  "summary": "Personalized large language models adapt responses to users' preferences and social attributes, but can introduce substantial universal truth inconsistencies across social groups, where some groups systematically receive less accurate responses on objective tasks. Existing alignment methods either ignore personalization or mainly focus on subjective preference alignment, largely overlooking fairness and consistency in universal truths. To address this gap, we study Truth-Invariant Alignment (TIA",
  "authors": "Thi-Nhung Nguyen, Linhao Luo, Rollin Omari, Junae Kim, Thuy-Trang Vu, Dinh Phung",
  "category": "research",
  "topics": "bias-fairness,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-01T06:19:52.000Z",
  "fetched_at": "2026-07-14T16:30:09.960Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3305",
  "original_url": "https://arxiv.org/abs/2606.01755v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}