Using a Local LLM as a Zero-Shot Classifier

Using a locally hosted LLM as a zero-shot classifier solves a problem that traditional clustering and keyword matching can't: making sense of thousands of short, varied text entries that express the same idea in different words. By defining categories upfront and letting the model handle paraphrase variation, roughly 7,000 entries were classified in about 45 minutes, revealing that half the dataset fell into just two categories.