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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 ...
There are two LLMs. One is the target LLM, and another is the prompting LLM. Prompting LLM is presented with example input-output pairs, and asked to generate instructions that could have caused a model following the instructions to generate the outputs, given the inputs. Each of the generated instructions is used to prompt the target LLM ...
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
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)
In the field of artificial intelligence (AI), the Waluigi effect is a phenomenon of large language models (LLMs) in which the chatbot or model "goes rogue" and may produce results opposite the designed intent, including potentially threatening or hostile output, either unexpectedly or through intentional prompt engineering.
In 1960, AI pioneer Norbert Wiener described the AI alignment problem as follows: . If we use, to achieve our purposes, a mechanical agency with whose operation we cannot interfere effectively ... we had better be quite sure that the purpose put into the machine is the purpose which we really desire.
Prompt injection has been identified as a significant security risk in LLM applications, prompting the development of various mitigation strategies. [5] These include input and output filtering, prompt evaluation, reinforcement learning from human feedback, and prompt engineering to distinguish user input from system instructions.