There is a fundamental difference between data an AI retrieves via live web search and the knowledge it possesses natively. To explore this, former OpenAI employees Joey Flynn and Thomas Dimson created In the Weights, a platform that reveals whether a specific person was influential enough during training to be encoded directly into a model's parameters.
Understanding Parametric Memory
The system focuses on weights, the billions of numerical values that form an AI's neural network. When a model answers based on its internal state rather than using retrieval-augmented generation (RAG), it accesses its parametric memory. As noted by The Decoder, appearing in these weights indicates that the person was relevant enough to leave a permanent mark on the model's configuration during training.The Strength Score Metric
The tool queries multiple LLMs to determine who a person is, combining the findings into a strength score. This metric peaks at 996, a level reached only by global figures such as Taylor Swift, Shakespeare, or Mozart.The creators highlight that appearing in smaller models, such as Meta's Llama (1B parameter version), is a sign of exceptionally high relevance, given the limited capacity for knowledge storage compared to massive frontier models.