{
  "id": 9432,
  "url": "https://doi.org/10.1145/3582269.3615599",
  "title": "Gender bias and stereotypes in Large Language Models",
  "summary": "Large Language Models (LLMs) have made substantial progress in the past several months, shattering state-of-the-art benchmarks in many domains. This paper investigates LLMs’ behavior with respect to gender stereotypes, a known issue for prior models. We use a simple paradigm to test the presence of gender bias, building on but differing from WinoBias, a commonly used gender bias dataset, which is likely to be included in the training data of current LLMs. We test four recently published LLMs and",
  "authors": "Hadas Kotek, Rikker Dockum, David Sun",
  "category": "research",
  "topics": "bias-fairness,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2023-10-13T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:33:54.155Z",
  "source_slug": "openalex",
  "source_name": "OpenAlex",
  "source_homepage": "https://openalex.org",
  "ethics_ai_record_url": "https://ethics.ai/record/9432",
  "original_url": "https://doi.org/10.1145/3582269.3615599",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}