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In "auto-CoT", [46] a library of questions are converted to vectors by a model such as BERT. The question vectors are clustered. Questions nearest to the centroids of each cluster are selected. An LLM does zero-shot CoT on each question. The resulting CoT examples are added to the dataset. When prompted with a new question, CoT examples to the ...
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.
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)
Field test – Modeler performs data gathering of subject under test Post-test modeling – Subject under test model input parameters are matched with subject under test–field–test output values Model validation/accreditation – Modeler provides sufficient evidence to a tester that a simulation adequately replicates field testing
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.
Wilson (2015) argues that such use of Vuong's test is invalid as a non-zero-inflated model is neither strictly non-nested nor partially non-nested in its zero-inflated counterpart. The core of the misunderstanding appears to be the terminology, which offers itself to being incorrectly understood to imply that all pairs of non-nested models are ...
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 ...