Gen Z, But Make It Statistical: Teaching LLMs to Listen to Data
A pricing team gives an LLM several hundred property listings and asks a sensible question: Which characteristics help predict the selling price? The model returns an equally sensible list. Swimming pools. Granite countertops. Scenic views. Green lawns. Kitchen islands. Everything sounds plausible. That is the problem. The list describes what generally makes a house attractive. It does not necessarily describe what separated expensive from inexpensive houses in this particular collection, sold in particular locations, during a particular year. The LLM has supplied real-estate conventional wisdom when the business needed dataset-specific evidence. ...