Zero-Shot Classification

Zero-shot classification asks a model to categorize text into labels it has never been explicitly trained on — the ultimate test of language understanding and generalization. The breakthrough was the natural language inference (NLI) trick: reframe classification as "does this text entail the label?" using models fine-tuned on MNLI, pioneered by Yin et al. (2019) and popularized by BART-large-MNLI. Today, instruction-tuned LLMs have largely subsumed this approach — GPT-4, Claude, and Llama 3 can classify into arbitrary taxonomies via prompting with near-supervised accuracy. The remaining challenge is consistency and calibration: LLMs are powerful but their predictions can be brittle to prompt phrasing, making them unreliable for high-stakes automated pipelines without careful engineering.