{
  "id": 16128,
  "url": "https://arxiv.org/abs/2608.01046v1",
  "title": "DeBERTa-Sentinel: Toward Transparent and Trustworthy Detection of AI-Generated Text",
  "summary": "The rapid spread of large language models (LLMs) across the web raises concerns about misinformation, academic integrity, automated content manipulation, and risks to vulnerable online communities. Existing transformer-based detectors, such as GPT-Sentinel, show promise but struggle to generalize to diverse model outputs and paraphrasing attacks, limiting their role in building trustworthy web ecosystems. This work introduces DeBERTa-Sentinel, a responsible AI-generated text detection framework",
  "authors": "Muhammad Yousaf Rehman, Muhammad Islam",
  "category": "research",
  "topics": "misinformation,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-02T07:08:20.000Z",
  "fetched_at": "2026-08-04T05:10:21.797Z",
  "source_slug": "x-arxiv-cs-cl-ethics-relevant-nlp",
  "source_name": "arXiv cs.CL (ethics-relevant NLP)",
  "source_homepage": "https://arxiv.org/list/cs.CL/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16128",
  "original_url": "https://arxiv.org/abs/2608.01046v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}