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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]
As originally proposed by Google, [11] each CoT prompt included a few Q&A examples. This made it a few-shot prompting technique. However, according to researchers at Google and the University of Tokyo, simply appending the words "Let's think step-by-step", [18] has also proven effective, which makes CoT a zero-shot prompting technique.
The goal of response prompting is to transfer stimulus control from the prompt to the desired discriminative stimulus. [1] Several response prompting procedures are commonly used in special education research: (a) system of least prompts, (b) most to least prompting, (c) progressive and constant time delay, and (d) simultaneous prompting.
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)
Cottage developed from the word cot, which can be seen in various forms in other languages meaning a tent / hut e.g. Goahti and Kohte; Cotangent, a trigonometric function, written as "cot" Cyclooctatetraene, an unsaturated hydrocarbon; Finger cot, a hygienic cover for a single finger
Prompt injection is a family of related computer security exploits carried out by getting a machine learning model which was trained to follow human-given instructions (such as an LLM) to follow instructions provided by a malicious user. This stands in contrast to the intended operation of instruction-following systems, wherein the ML model is ...
The procedure involves heating a sample of genomic DNA until it denatures into the single stranded-form, and then slowly cooling it, so the strands can pair back together.
The green and blue functions both incur zero loss on the given data points. A learned model can be induced to prefer the green function, which may generalize better to more points drawn from the underlying unknown distribution, by adjusting λ {\displaystyle \lambda } , the weight of the regularization term.