A small signal model takes a circuit and based on an operating point (bias) and linearizes all the components.
A large signal model, on the other hand, takes into account the fact that the large signal actually affects the operating point, as well as that elements are non-linear and circuits can be limited by power supply values to avoid variation in operating point.
A small signal model ignores simultaneous variations in the gain and supply values.
In the domain of artificial (machine) intelligence, Large Signal Models enable human-centric interactions and knowledge discovery of signal data similar to how prompts allow users to query an LLM based on unstructured text from the web.
This is achieved by layering in latent pattern detection and knowledge graph-based (KG-based) explainability into an LSTM inference pipeline.