{
  "id": 7532,
  "url": "https://arxiv.org/abs/2603.07792v1",
  "title": "Dual-Metric Evaluation of Social Bias in Large Language Models: Evidence from an Underrepresented Nepali Cultural Context",
  "summary": "Large language models (LLMs) increasingly influence global digital ecosystems, yet their potential to perpetuate social and cultural biases remains poorly understood in underrepresented contexts. This study presents a systematic analysis of representational biases in seven state-of-the-art LLMs: GPT-4o-mini, Claude-3-Sonnet, Claude-4-Sonnet, Gemini-2.0-Flash, Gemini-2.0-Lite, Llama-3-70B, and Mistral-Nemo in the Nepali cultural context. Using Croissant-compliant dataset of 2400+ stereotypical an",
  "authors": "Ashish Pandey, Tek Raj Chhetri",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": "google,mistral",
  "regions": null,
  "published_at": "2026-03-08T20:26:13.000Z",
  "fetched_at": "2026-07-14T16:33:16.669Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7532",
  "original_url": "https://arxiv.org/abs/2603.07792v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}