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GPT-3 is capable of performing zero-shot and few-shot learning (including one-shot). [1] In June 2022, Almira Osmanovic Thunström wrote that GPT-3 was the primary author on an article on itself, that they had submitted it for publication, [24] and that it had been pre-published while waiting for completion of its review. [25]
Few-shot learning A prompt may include a few examples for a model to learn from, such as asking the model to complete " maison → house, chat → cat, chien →" (the expected response being dog ), [ 31 ] an approach called few-shot learning .
It uses an encoder-decoder to accomplish few-shot learning. The encoder outputs a representation of the input that the decoder uses as input to perform a specific task, such as translating the input into another language. The model outperforms the much larger GPT-3 in language translation and summarization.
Prompt injection is a family of related computer security exploits carried out by getting a machine learning model (such as an LLM) which was trained to follow human-given instructions 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 ...
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
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)
GPT-3: May 2020: OpenAI: 175 [20] 300 billion tokens [17] 3640 [21] proprietary A fine-tuned variant of GPT-3, termed GPT-3.5, was made available to the public through a web interface called ChatGPT in 2022. [22] GPT-Neo: March 2021: EleutherAI: 2.7 [23] 825 GiB [24] MIT [25] The first of a series of free GPT-3 alternatives released by ...
"We participated in the 12th BioASQ challenge, which is a retrieval augmented generation (RAG) setting, and explored the performance of current GPT models Claude 3 Opus, GPT-3.5-turbo and Mixtral 8x7b with in-context learning (zero-shot, few-shot) and QLoRa fine-tuning. We also explored how additional relevant knowledge from Wikipedia added to ...