Zero-shot learning is an advanced machine learning technique where a model is able to perform tasks it has never been explicitly trained on. Instead of relying on task-specific training data, zero-shot learning leverages generalized knowledge acquired during pretraining—often from large-scale language or vision models—and applies it to completely new scenarios using natural language instructions or prompts. This allows AI systems to handle novel inputs without additional labeled examples, making them more scalable and adaptable across a wide range of tasks.
Zero-shot capabilities are especially prominent in large language models (LLMs) like ChatGPT, which can answer questions, summarize content, or classify data with no prior examples. The technique plays a critical role in enabling broader AI usability, reducing development costs, and accelerating deployment in business, research, and real-time applications where training data may not be readily available.