{
  "id": 491,
  "url": "https://arxiv.org/abs/2606.28992v1",
  "title": "Fine-Tuning General-Purpose Large Language Models for Agricultural Applications:A Reproducible Framework and Evaluation Protocol Based on Qwen3-8B",
  "summary": "General-purpose large language models (LLMs) have demonstrated strong abilities in opendomain question answering, information extraction, and text generation. Agricultural applications, however, are domain-specific, region-dependent, time-sensitive, and safety-critical. Without data governance, expert evaluation, and evidence constraints, an agricultural assistant mayproduce unreliable advice on crop diseases, pesticide use, fertilization, or policy interpretation.To avoid presenting unverified ",
  "authors": "Zhaoyang Li, Ruijie Zhang, Jiaqi Liu, Zhaoji Sun",
  "category": "research",
  "topics": "regulation",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-27T16:02:50.000Z",
  "fetched_at": "2026-07-14T14:14:32.650Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/491",
  "original_url": "https://arxiv.org/abs/2606.28992v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}