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Prompt engineering can possibly be further enabled by in-context learning, defined as a model's ability to temporarily learn from prompts. The ability for in-context learning is an emergent ability [ 62 ] of large language models .
The name is a play on words based on the earlier concept of one-shot learning, in which classification can be learned from only one, or a few, examples. Zero-shot methods generally work by associating observed and non-observed classes through some form of auxiliary information, which encodes observable distinguishing properties of objects. [1]
A generative LLM can be prompted in a zero-shot fashion by just asking it to translate a text into another language without giving any further examples in the prompt. Or one can include one or several example translations in the prompt before asking to translate the text in question. This is then called one-shot or few-shot learning, respectively.
Few-shot learning and one-shot learning may refer to: Few-shot learning, a form of prompt engineering in generative AI; One-shot learning (computer vision)
One-shot learning is an object categorization problem, found mostly in computer vision. Whereas most machine learning -based object categorization algorithms require training on hundreds or thousands of examples, one-shot learning aims to classify objects from one, or only a few, examples.
It has a context window size of 2048 tokens, and has demonstrated strong "zero-shot" and "few-shot" learning abilities on many tasks. [2] On September 22, 2020, Microsoft announced that it had licensed GPT-3 exclusively. Others can still receive output from its public API, but only Microsoft has access to the underlying model. [5]
In the context of neural networks, self-supervised learning aims to leverage inherent structures or relationships within the input data to create meaningful training signals. SSL tasks are designed so that solving them requires capturing essential features or relationships in the data.
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2]