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Few-shot learning [ edit ] 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 ), [ 24 ] an approach called few-shot learning .
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 largest and most capable LLMs are generative pretrained transformers (GPTs).
A generative LLM can be prompted in a zero-shot fashion by just asking it to translate a text into another language without giving any further examples in the prompt. Or one can include one or several example translations in the prompt before asking to translate the text in question. This is then called one-shot or few-shot learning, respectively.
The Pile is an 886.03 GB diverse, open-source dataset of English text created as a training dataset for large language models (LLMs). It was constructed by EleutherAI in 2020 and publicly released on December 31 of that year. [1] [2] It is composed of 22 smaller datasets, including 14 new ones. [1]
The 2024-25 NBA season starts on Oct. 22. For the next few weeks, we'll be breaking down the biggest questions, best- and worst-case scenarios, and fantasy outlooks for all 30 teams — plus ...
The first shot was a 10-ft bank shot from the right angle, the second was straight-on jump shot from the top of the key, the third was an NBA three-point shot from the left angle and the fourth is a half-court shot. There was a two-minute time limit for each attempt and the top two times advanced to a head-to-head final round.
The evening includes three staples: the Kia Skills Challenge, Starry 3-Point Contest and AT&T Slam Dunk contest. There's also a new event: NBA career 3-point leader Steph Curry and WNBA star ...
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)