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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", [21] has also proven effective, which makes CoT a zero-shot prompting technique.
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]
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.
The two types of prompting in a behavior chain are either most to least(MTL) or least to most (LTM). MTL prompting is when the most intrusive prompt is introduced initially and then systematically faded out to least intrusive prompts. This prompting method is mainly used when the task analysis is being taught. [5]
Download as PDF; Printable version; In other projects ... move to sidebar hide. Few-shot learning and one-shot learning may refer to: Few-shot learning, a form of ...
CLIP can perform zero-shot image classification tasks. This is achieved by prompting the text encoder with class names and selecting the class whose embedding is closest to the image embedding. For example, to classify an image, they compared the embedding of the image with the embedding of the text "A photo of a {class}.", and the {class} that ...
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 foundation of the uncertainty reduction theory stems from the information theory, originated by Claude E. Shannon and Warren Weaver. [2] Shannon and Weaver suggests, when people interact initially, uncertainties exist especially when the probability for alternatives in a situation is high and the probability of them occurring is equally high. [6]