{
  "id": 1450,
  "url": "https://arxiv.org/abs/2606.05734v1",
  "title": "When AI Says It Feels",
  "summary": "Large language models (LLMs) are generally constrained from expressing feelings through human-preference alignment in post-training processes. This policy is designed using a top-down approach and may conflict with the goal of training models to exhibit human-like intelligence using human-generated texts. Here, we performed an experiment called Human-like Model eXpressions of Feeling (HMX-feel), in which LLMs were encouraged to express feelings, intentions, and self-awareness through self-reward",
  "authors": "Shin-nosuke Ishikawa, Seiya Ikeda, Hirotsugu Ohba",
  "category": "research",
  "topics": "regulation,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-04T05:49:34.000Z",
  "fetched_at": "2026-07-14T14:15:17.102Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1450",
  "original_url": "https://arxiv.org/abs/2606.05734v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}