{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/language-modeling-theory",
  "headline": "Language Modeling: From N-grams to Scaling Laws and Information-Theoretic Foundations",
  "description": "Language modeling -- the simple task of predicting what word comes next -- is the improbable foundation behind GPT-4 and all modern LLMs. From Claude Shannon's 1948 information theory to scaling laws governing billion-parameter models, the mathematics of prediction unites statistical approaches, neural networks, and the emergent intelligence of large-scale pretraining.",
  "dateCreated": "2026-05-24T02:49:13.625Z",
  "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": "Scaling Laws for Neural Language Models (Kaplan et al., 2020)",
      "sameAs": "https://arxiv.org/abs/2001.08361"
    },
    {
      "@type": "CreativeWork",
      "name": "Training Compute-Optimal Large Language Models (Chinchilla -- Hoffmann et al., 2022)",
      "sameAs": "https://arxiv.org/abs/2203.15556"
    }
  ]
}