Sure, I can try to explain more about zero-shot learning. Zero-shot learning is a **machine learning paradigm** where a model is trained on a set of labeled examples but is then able to classify new examples from previously unseen classes. For example, given a set of images of animals to be classified, along with auxiliary textual descriptions of what animals look like, a model which has been trained to recognize horses, but has never been given a zebra, can still recognize a zebra when it also knows that zebras look like striped horses.

One way to implement zero-shot learning is to use a **pre-trained language model** that can encode both the input text and the candidate labels into a common semantic space. Then, the model can compare the similarity between the input text and each label and output the most likely one. For example, given the input text "I have a problem with my iphone that needs to be resolved asap!" and the candidate labels ["urgent", "not urgent", "phone", "tablet", "computer"], the model can output "urgent" and "phone" as the most probable labels.

Zero-shot learning is useful for situations where the amount of labeled data is small or where new classes emerge frequently. It also mimics how humans can learn from few or no examples by using prior knowledge and reasoning.
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