{
  "id": 14115,
  "url": "https://arxiv.org/abs/2508.05775",
  "title": "Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM",
  "summary": "arXiv:2508.05775v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have revolutionized content creation across digital platforms, offering unprecedented capabilities in natural language generation and understanding. Meanwhile, they pose risks by inadvertently producing toxic, offensive, or biased content. This dual role of LLMs, both as powerful tools for text generation and as potential sources of harmful language, presents a pressing sociotechnical challenge. In this survey",
  "authors": "Chi Zhang, Changjia Zhu, Junjie Xiong, Xiaoran Xu, Lingyao Li, Yao Liu, Zhuo Lu",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-29T04:00:00.000Z",
  "fetched_at": "2026-07-29T05:10:12.205Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14115",
  "original_url": "https://arxiv.org/abs/2508.05775",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}