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{{see also|Machine learning terms}} | |||
==Introduction== | |||
A '''language model''' in the context of machine learning is a computational model designed to understand and generate human language. Language models leverage statistical and probabilistic techniques to analyze, process, and produce text or speech data, making them indispensable in a wide range of natural language processing (NLP) tasks. Over time, the development of increasingly sophisticated models has led to significant advancements in the field of artificial intelligence (AI). | |||
==Overview of Language Models== | |||
Language models can be divided into two broad categories based on their underlying techniques: '''statistical language models''' and '''neural language models'''. | |||
===Statistical Language Models=== | |||
Statistical language models, such as '''n-gram models''' and '''hidden Markov models (HMMs)''', employ statistical methods to estimate the probability of a sequence of words in a given language. These models generally rely on the analysis of large corpora of text data to identify patterns and probabilities that can be used to generate or evaluate novel sentences. | |||
[[N-gram model]]s are based on the assumption that the probability of a word occurring in a sequence depends only on a fixed number of preceding words, known as the n-1 context. This simplification allows for efficient computation and storage of word probabilities but often results in limited expressiveness and adaptability to long-term dependencies. | |||
===Neural Language Models=== | |||
Neural language models, on the other hand, leverage deep learning techniques and neural networks to capture and represent complex linguistic patterns. These models, which include '''recurrent neural networks (RNNs)''', '''long short-term memory (LSTM) networks''', '''gated recurrent units (GRUs)''', and '''transformer models''', can effectively learn and represent long-range dependencies in language data. | |||
[[Transformer model]]s, in particular, have gained prominence in recent years due to their ability to handle large-scale language tasks with high accuracy. Notable examples of transformer models include the '''OpenAI GPT-series''' and '''Google BERT'''. | |||
==Applications of Language Models== | |||
Language models have a broad range of applications in NLP, including: | |||
* '''Machine translation''': Converting text from one language to another. | |||
* '''Text summarization''': Creating concise summaries of longer documents or articles. | |||
* '''Speech recognition''': Translating spoken language into written text. | |||
* '''Sentiment analysis''': Determining the sentiment or emotion conveyed in a piece of text. | |||
* '''Text generation''': Creating coherent and contextually relevant text based on a given input. | |||
* '''Question answering''': Providing relevant answers to questions posed in natural language. | |||
==Explain Like I'm 5 (ELI5)== | |||
A language model is like a smart helper that learns to understand and use human language by looking at lots of examples. Some helpers use math tricks and shortcuts to figure out what words go together, while others use fancy computer brain techniques to learn more complicated patterns. These helpers can do all sorts of cool things, like translate between languages, write summaries, understand what people say, figure out how people feel, write stories, or answer questions. | |||
[[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]] | |||