Introduction to Large Language Models
Language models are statistical models that are used for natural language processing (NLP) tasks, such as speech recognition, machine translation, and text generation. A language model is a probability distribution over sequences of words, meaning that it assigns a probability to a given sequence of words. In other words, a language model predicts the likelihood of a given sequence of words occurring in a language.
For example, consider the sentence, "The cat sat on the mat." A language model would assign a higher probability to this sentence than to a sentence like, "The mat sat on the cat." because the first sentence is a more likely sequence of words in the English language.
Language models can be trained in many different ways. One popular approach is to use a neural network, which is a type of machine learning model that is loosely based on the structure of the human brain. Neural language models can be trained on large amounts of text data and can learn to generate coherent and grammatically correct sentences.
Another approach is to use a technique called n-grams. An n-gram is a sequence of n words, and n-gram language models assign probabilities to each n-gram in a sentence. For example, a 3-gram language model would assign probabilities to each sequence of three words in a sentence.
In summary, language models are statistical models that assign probabilities to sequences of words. They can be trained using neural networks or n-grams and are used for a variety of NLP tasks, including speech recognition, machine translation, and text generation.
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