{
  "id": 18797,
  "url": "https://arxiv.org/abs/2608.11631v1",
  "title": "CLAIM: Leading Open-domain Active Clarification of Large Language Models with Uncertainty Measurement",
  "summary": "In open-domain human-computer interaction scenarios, large language models (LLMs) frequently encounter user queries that are ambiguous or incomplete. In such cases, directly producing an answer often leads to overgeneralized, erroneous, or low-information responses. In contrast, asking clarifying questions can substantially improve interaction quality. However, existing approaches still rely heavily on manually annotated data or preference alignment to address two fundamental challenges: when cl",
  "authors": "Kuangzhao Yang, Ziliang Zhao, Zhicheng Dou",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T04:27:45.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18797",
  "original_url": "https://arxiv.org/abs/2608.11631v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}