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Contrastive Language-Image Pre-training (CLIP) is a technique for training a pair of neural network models, one for image understanding and one for text understanding
Contrastive Language-Image Pre-training (CLIP) allows joint pretraining of a text encoder and an image encoder, such that a matching image-text pair have image encoding vector and text encoding vector that span a small angle (having a large cosine similarity).
First described in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is an unsupervised transformer language model and the successor to GPT-2. [ 182 ] [ 183 ] [ 184 ] OpenAI stated that the full version of GPT-3 contained 175 billion parameters , [ 184 ] two orders of magnitude larger than the 1.5 billion [ 185 ] in the full version of ...
Contrastive linguistics, since its inception by Robert Lado in the 1950s, has often been linked to aspects of applied linguistics, e.g., to avoid interference errors in foreign-language learning, as advocated by Di Pietro (1971) [1] (see also contrastive analysis), to assist interlingual transfer in the process of translating texts from one ...
That development led to the emergence of large language models such as BERT (2018) [28] which was a pre-trained transformer (PT) but not designed to be generative (BERT was an "encoder-only" model). Also in 2018, OpenAI published Improving Language Understanding by Generative Pre-Training, which introduced GPT-1, the first in its GPT series. [29]
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The theoretical foundations for what became known as the contrastive analysis hypothesis were formulated in Robert Lado's Linguistics Across Cultures (1957). In this book, Lado claimed that "those elements which are similar to [the learner's] native language will be simple for him, and those elements that are different will be difficult".
The majority of the studies done on contrast and contrastive relations in semantics has concentrated on characterizing exactly which semantic relationships could give rise to contrast. Earliest studies in semantics also concentrated on identifying what distinguished clauses joined by and from clauses joined by but .