{
  "id": 7253,
  "url": "https://arxiv.org/abs/2603.13776v1",
  "title": "Retrieval-Feedback-Driven Distillation and Preference Alignment for Efficient LLM-based Query Expansion",
  "summary": "Large language models have recently enabled a generative paradigm for query expansion, but their high inference cost makes direct deployment difficult in practical retrieval systems. To address this issue, a retrieval-feedback-driven distillation and preference-alignment framework is proposed to transfer retrieval-friendly expansion behavior from a strong teacher model to a compact student model. Rather than relying on few-shot exemplars at inference time, the framework first leverages two compl",
  "authors": "Minghan Li, Guodong Zhou",
  "category": "research",
  "topics": "safety-alignment,children-education",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-14T05:59:12.000Z",
  "fetched_at": "2026-07-14T16:33:03.575Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7253",
  "original_url": "https://arxiv.org/abs/2603.13776v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}