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The term zero-shot learning itself first appeared in the literature in a 2009 paper from Palatucci, Hinton, Pomerleau, and Mitchell at NIPS’09. [5] This terminology was repeated later in another computer vision paper [6] and the term zero-shot learning caught on, as a take-off on one-shot learning that was introduced in computer vision years ...
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.
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)
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.
Model size License Comments Stanford bunny: 1993-94 [11] Greg Turk, Marc Levoy at Stanford University: Ceramic rabbit [12] 69,451 triangles [11] Figurine of unknown authorship and licensing status, scan itself released under a two-clause BSD license. A test of range scanning physical objects. Originally .ply file. Stanford dragon: 1996 [11 ...
A model describing a SUT is usually an abstract, partial presentation of the SUT's desired behavior. Test cases derived from such a model are functional tests on the same level of abstraction as the model. These test cases are collectively known as an abstract test suite. An abstract test suite cannot be directly executed against an SUT because ...
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 ...
There are several architectures that have been used to create Text-to-Video models. Similar to Text-to-Image models, these models can be trained using Recurrent Neural Networks (RNNs) such as long short-term memory (LSTM) networks, which has been used for Pixel Transformation Models and Stochastic Video Generation Models, which aid in consistency and realism respectively. [31]