Summary
Language models such as GPT-3 and GPT-2 are trained to predict the next word in a sentence, similar to how an autocomplete feature works.
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These models can also learn natural language processing (NLP) tasks without needing task-specific training data, instead learning from examples derived from raw text.
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According to
Summary
GPT-2 shows that much larger language models trained on a more diverse dataset derived from the internet begin to learn these NLP tasks without needing task-specific training data, instead learning from examples the system derives from the raw text.
Better Language Models and Their Implications
openai.com
Get state-of-the-art natural language processing without the need for expensive ... Large pre-trained Transformer language models, or simply large language ...
Introduction to Large Language Models
cohere.ai
A language model is a probability distribution over sequences of words.[1] Given any sequence of words of length m, a language model assigns a probability P ( w 1 , … , w m ) {\displaystyle P(w_{1},\ldots ,w_{m})} to the whole sequence. Language models generate probabilities by training on text corpora in one or many languages. Given that languages can be used to express an infinite variety of valid sentences (the property of digital infinity), language modeling faces the problem of assigning non-zero probabilities to linguistically valid sequences that may never be encountered in the training data. Several modelling approaches have been designed to surmount this problem, such as applying the Markov assumption or using neural architectures such as recurrent neural networks or transformers.
Language model - Wikipedia
wikipedia.org
A language model is a probability distribution over sequences of words.[1] Given any sequence of words of length m, a language model assigns a probability P ( w 1 , … , w m ) {\displaystyle P(w_{1},\ldots ,w_{m})} to the whole sequence. Language models generate probabilities by training on text corpora in one or many languages. Given that languages can be used to express an infinite variety of valid sentences (the property of digital infinity), language modeling faces the problem of assigning non-zero probabilities to linguistically valid sequences that may never be encountered in the training data. Several modelling approaches have been designed to surmount this problem, such as applying the Markov assumption or using neural architectures such as recurrent neural networks or transformers.
Do large language models understand us? | by Blaise Aguera y Arcas | Medium
medium.com
