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BERT is meant as a general pretrained model for various applications in natural language processing. That is, after pre-training, BERT can be fine-tuned with fewer resources on smaller datasets to optimize its performance on specific tasks such as natural language inference and text classification , and sequence-to-sequence-based language ...
Prompt engineering is the process of structuring or crafting an instruction in order to produce the best possible output from a generative artificial intelligence (AI) model. [1] A prompt is natural language text describing the task that an AI should perform. [2]
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. As language models, LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a self-supervised and semi-supervised training process. [1]
A language model is a probabilistic model of a natural language. [1] In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text.
The input hypothesis, also known as the monitor model, is a group of five hypotheses of second-language acquisition developed by the linguist Stephen Krashen in the 1970s and 1980s. Krashen originally formulated the input hypothesis as just one of the five hypotheses, but over time the term has come to refer to the five hypotheses as a group.
Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP) that is concerned with building systems that automatically answer questions that are posed by humans in a natural language. [1]
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The model, as well as the code base and the data used to train it, are distributed under free licences. [3] BLOOM was trained on approximately 366 billion (1.6TB) tokens from March to July 2022. [4] [5] BLOOM is the main outcome of the BigScience collaborative initiative, [6] a one-year-long research workshop that took place between May 2021 ...