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A prompt for a text-to-text language model can be a query, a command, or a longer statement including context, instructions, and conversation history. Prompt engineering may involve phrasing a query, specifying a style, choice of words and grammar, [3] providing relevant context, or describing a character for the AI to mimic. [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)
Prompt injection can be viewed as a code injection attack using adversarial prompt engineering. In 2022, the NCC Group characterized prompt injection as a new class of vulnerability of AI/ML systems. [10] The concept of prompt injection was first discovered by Jonathan Cefalu from Preamble in May 2022 in a letter to OpenAI who called it command ...
For example, asking the student the question "what is this?" (this would be an example of an impure tact) has been used to prompt a correct tact response (this prompt can be faded until the learner can emit a pure tact). Echoic prompts (teacher repeats the correct answer which the learner must echo) have also been used to train tact responses.
The rituals of self-discipline were nothing new. He’d kept a journal since the 8th grade documenting his daily meals and workout routines. As a teenager, he’d woken up to the words of legendary coaches he’d copied from books and taped to his bedroom walls — John Wooden on preparation, Vince Lombardi on sacrifice and Dan Gable on goals.
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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]