Unverified50% confidenceSolutionExact time
The recommended path is to use zero-shot for rapid prototyping in the early stage, use zero-shot outputs as weak annotations to build an initial training set in the middle stage, and migrate to a dedicated classifier in the mature stage to reduce costs and improve efficiency.
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10/5/2026
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UnverifiedWhen data is sufficient, categories are stable, cost sensitivity is high, and traffic is large, training a classifier is preferred; when there is no data, rapid validation is needed, or categories change frequently, zero-shot is a more appropriate starting point.75% similarUnverifiedThe zero-shot capability of the meta-learning approach is essentially limited by the coverage of the system distribution seen during training, while the structural prior approach has stronger extrapolation capability for classes of systems where the prior assumptions hold.74% similarUnverifiedTraining from scratch means not using any pretrained weights, but instead completing the full training process on a new dataset with randomly initialized parameters.73% similarUnverifiedIn zero-shot scenarios, retrieval training can improve the model's matching capability for options with content.73% similarVerifiedOne-shot learning is not the same as zero-shot learning; the single demonstration relies on prior knowledge accumulated by the model from massive pretraining data.73% similar
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