Zero-shot Inference is about applying a pre-trained model to tasks or data types it wasn’t explicitly trained for, without any additional training. It’s more about the versatility of a model in handling tasks it wasn’t directly trained on.
Application: An example is using a language model trained on a wide range of texts to perform specific tasks like translation, summarization, or question answering, without additional task-specific training.
How It Works: This capability often hinges on the breadth and depth of the training data and the model’s ability to generalize from that data to new tasks. The model leverages its existing knowledge and understanding to make inferences about new, unseen tasks or data types.
See Also: Zero-shot Learning