{
  "id": "anthropic-sitemap:research:toy-models-of-superposition",
  "type": "article",
  "title": "Toy Models of Superposition",
  "abstract": "In this paper, we use toy models — small ReLU networks trained on synthetic data with sparse input features — to investigate how and when models represent more features than they have dimensions. We call this phenomenon superposition. When features are sparse, superposition allows compression beyond what a linear model would do, at the cost of \"interference\" that requires nonlinear filtering.",
  "issued": {
    "date-parts": [
      [
        2022,
        9,
        14
      ]
    ]
  },
  "URL": "https://www.anthropic.com/research/toy-models-of-superposition",
  "publisher": "Anthropic",
  "source": "vendor/anthropic-sitemap/research/toy-models-of-superposition.md"
}