What is Zero-Shot Learning?
Zero-shot learning is the ability of language models to perform tasks without being provided any examples, relying solely on the task instructions or description in the prompt. Unlike few-shot learning which provides examples to demonstrate the desired behavior, zero-shot approaches simply describe what is needed and trust the model to understand and execute based on its pre-trained knowledge and instruction-following capabilities. This represents the most minimal form of task specification.
The capability emerges from models trained on diverse tasks and instruction-tuning procedures that teach models to follow natural language instructions. When given a clear description of a task, capable models can often perform it reasonably well without examples. For instance, a zero-shot prompt might simply state "Translate the following English text to French:" without providing any translation examples. The model uses its understanding of the instruction and knowledge of both languages to perform the task.
Zero-shot learning offers significant advantages in simplicity and efficiency. It requires minimal prompt engineering, consumes less context window space than few-shot approaches, and enables rapid adaptation to new tasks without gathering examples. However, performance is generally lower than few-shot learning for complex or specialized tasks, as examples provide valuable clarification of expectations and desired formats. The choice between zero-shot and few-shot approaches depends on task complexity, model capabilities, and whether suitable examples are available. Modern powerful models have dramatically improved zero-shot capabilities, making this approach viable for many applications.