{
  "id": 6292,
  "url": "https://arxiv.org/abs/2604.05224v1",
  "title": "Attribution Bias in Large Language Models",
  "summary": "As Large Language Models (LLMs) are increasingly used to support search and information retrieval, it is critical that they accurately attribute content to its original authors. In this work, we introduce AttriBench, the first fame- and demographically-balanced quote attribution benchmark dataset. Through explicitly balancing author fame and demographics, AttriBench enables controlled investigation of demographic bias in quote attribution. Using this dataset, we evaluate 11 widely used LLMs acro",
  "authors": "Eliza Berman, Bella Chang, Daniel B. Neill, Emily Black",
  "category": "research",
  "topics": "bias-fairness,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-06T22:40:03.000Z",
  "fetched_at": "2026-07-14T16:32:24.289Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6292",
  "original_url": "https://arxiv.org/abs/2604.05224v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}