{
  "id": 6702084,
  "title": "Domain-Specific Hallucination Detection in Large Language Models",
  "url": "https://urgent.news/2026/09/10/domain-specific-hallucination-detection-in-large-language-models",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-10T17:45:36.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.11878v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Large language models generate fluent text that can contain unfaithful claims -- a phenomenon known as hallucination. We present a multi-signal detection pipeline combining fine-tuned DeBERTa-v3 classification, Monte Carlo (MC) Dropout uncertainty quantification, and temperature-scaled calibration for response-level hallucination detection. Evaluated on the HaluEval benchmark, our pipeline…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}