Author: Zen and the Art of Computer Programming
1 Introduction
Language Model (Language Model) is an important technology in natural language processing tasks. It is a prediction model based on a statistical language model, which can sort any given sentence or paragraph according to a certain probability distribution, and assign each word in the input sentence its ranking in the entire vocabulary, and finally give the corresponding probability value. Using language models can help people understand text more accurately, make decisions, and provide references for natural language processing tasks such as machine translation and question answering systems.
A language model is essentially a probabilistic model that estimates the probability of a language generating text based on a large amount of existing text data. Language models are the basis of many NLP tasks, such as information retrieval, text summarization, automatic summarization, translation, intent recognition, text classification, etc. Training language models often consumes huge time and resources, and generally requires hundreds of thousands to millions of sample data, so it has become one of the most expensive and challenging tasks in the field of artificial intelligence.
In recent years, with the improvement of computer hardware performance and the continuous growth of the open source community, the language model training technology has also been rapidly updated. The wide application of deep learning technology makes the training of language models possible.
2. Basic Concepts and Terminology
In order to better understand the relevant knowledge of the language model, you first need to have a clear understanding of the relevant terms of the language model. The following is a brief introduction to related terms:
- Corpus (Corpus): a collection of text data.
- Vocabulary: A collection of all words that have appeared.
- Token Sequence: A sequence consisting of one or more words. For example: "I love you" is a sequence of tokens.
- Language Model (Language Model): Given a tag sequence, calculate the probability distribution of the sequence, and give a ranking of the order in which words appear. For example: a model trained on a given corpus.
- n-gram language model: n-gram language model is a specific language model, it believes that the current word depends on the previous