{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/text-classification",
  "headline": "Text Classification: Zero-Shot, Few-Shot, and LLM-Based Document Categorization",
  "description": "Text classification -- assigning categories to documents -- is one of NLP's oldest and most practical tasks. The LLM era has transformed it: zero-shot classification eliminates the need for labeled data, few-shot approaches match traditional fine-tuning with 8 examples, and LLMs can classify documents based on natural language instructions rather than fixed category sets.",
  "dateCreated": "2026-05-24T02:49:13.665Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "Text Classification in the LLM Era -- Where do we stand?",
      "sameAs": "https://arxiv.org/abs/2502.11830"
    },
    {
      "@type": "CreativeWork",
      "name": "A review on NLP zero-shot and few-shot learning: methods and applications",
      "sameAs": "https://link.springer.com/article/10.1007/s42452-025-07225-5"
    }
  ]
}