{
  "id": 661,
  "url": "https://arxiv.org/abs/2606.24267v1",
  "title": "Pigeonholing: Bad prompts hurt models to collapse and make mistakes",
  "summary": "While in-context learning is generally shown to be effective in Large Language Models (LLMs), bad contexts can cause performance degradation and mode collapse, a phenomenon we call \"pigeonholing.\" **Unintentionally bad** contexts can happen without malicious jailbreaking intents: For example, a user asks the model to justify an incorrect math theorem or fails to correct the model's buggy code. Specifically, we investigate ``pigeonholing\" in two scenarios: (1) when the user suggests a solution, a",
  "authors": "Hyunji Nam, Keertana Chidambaram, Dorottya Demszky, Natasha Jaques",
  "category": "research",
  "topics": "safety-alignment,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-23T07:52:22.000Z",
  "fetched_at": "2026-07-14T14:14:41.552Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/661",
  "original_url": "https://arxiv.org/abs/2606.24267v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}