{
  "id": 775,
  "url": "https://arxiv.org/abs/2606.21147v1",
  "title": "AOR-Bench: Do Large Audio Language Models Over-Refuse Pseudo-Harmful Queries?",
  "summary": "Large Audio Language Models (LALMs) have demonstrated strong performance across a wide range of audio tasks. As they are increasingly deployed in real-world applications, ensuring their safety alignment has become more important. Although refusal mechanisms serve as a key safeguard by preventing LALMs from responding to harmful requests, they can also lead to {\\em over-refusal}, where models incorrectly reject benign queries. This issue is especially challenging in the audio domain because speec",
  "authors": "Jiaxi Yang, Chaewan Chun, Jason Lucas, Yuchen Yang, Dongwon Lee",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-19T06:37:32.000Z",
  "fetched_at": "2026-07-14T14:14:46.035Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/775",
  "original_url": "https://arxiv.org/abs/2606.21147v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}