"A language model is a probabilistic model of a natural language that can generate probabilities of a series of words, based on text corpora in one or multiple languages it was trained on."
Statistical models used to predict the probability of a sequence of words in a language, including n-gram models and language models based on neural networks.
Natural Language Processing (NLP): A field of study concerned with the interactions between computers and human (natural) languages, including natural language understanding, natural language generation, and machine translation.
Probability and Statistics: The mathematical framework essential for understanding and building language models, including topics such as probability distributions, conditional probability, and hypothesis testing.
Linguistic Structure: The study of language structure, including morphology, syntax, and semantics. Understanding linguistic structure is crucial for building accurate and effective language models.
Corpus Linguistics: A research area in linguistics that involves the analysis of large collections of text, or corpora, to identify patterns and relationships between words and phrases. Corpus linguistic methods are often used in the development of language models.
Machine Learning: A subfield of computer science that involves the development of algorithms and models that enable computers to learn from data. Machine learning is used extensively in NLP, especially in the development of language models.
Deep Learning: A subset of machine learning that involves the use of artificial neural networks to learn from data. Deep learning has proven to be highly effective in NLP, especially in the development of language models.
Recurrent Neural Networks (RNNs): A type of artificial neural network that is particularly well-suited to sequential data, such as text. RNNs have proven to be highly effective in the development of language models.
Long Short-Term Memory (LSTM) Networks: A type of recurrent neural network that is designed to overcome the limitations of traditional RNNs by selectively remembering or forgetting information over longer periods of time.
Attention Mechanisms: A technique used in deep learning that allows models to selectively focus on different parts of input data. Attention mechanisms have proven to be highly effective in language modeling tasks.
Transformer Networks: A type of deep learning architecture introduced in 2017 that has quickly become a standard approach for language modeling tasks. Transformers use attention mechanisms to selectively focus on different parts of input data and achieve state-of-the-art performance on a wide range of NLP tasks.
Rule-based model: This type of language model is created through a set of predetermined rules that govern how a given language should be interpreted and processed. It relies on handcrafted grammars to perform language processing tasks.
Statistical model: This model uses probability and statistics to analyze and process language. It is based on large amounts of data to learn patterns in language and make predictions.
Neural network model: This model uses deep neural networks to understand and process natural language. It has become popular in recent years and has achieved state-of-the-art performance on many natural language tasks.
Markov model: This model uses Markov chains to predict the next word or sequence of words based on the previous words in a sentence or text.
Hidden Markov model: This model uses a probabilistic model where the state of the language is hidden, and the observations are the visible output. It is often used in speech recognition and natural language generation.
Long short-term memory (LSTM): This model uses a type of neural network designed to handle sequence data like natural language. It can remember long-term dependencies and can be trained on large amounts of data.
Bidirectional encoder representations from transformers (BERT): This model uses a transformer architecture for natural language processing. It can learn from both directions of the text and has achieved state-of-the-art results on many NLP tasks.
Generative model: This model generates new natural language sentences or texts. It can be trained on large amounts of data and can produce human-like and coherent responses.
Topic model: This model identifies topics in a collection of documents. It is often used for document clustering, text classification, and information retrieval.
Latent Dirichlet Allocation (LDA): This model is a type of topic model that uses probabilistic modeling to discover the underlying topic of a document. It is often used for topic modeling and document clustering.
Word embedding model: This model represents words as vectors in a high-dimensional space. It can capture semantic and syntactic relationships between words and is used for various NLP tasks like sentiment analysis, text classification, and language translation.
Sequence to sequence model: This model takes a sequence of words as input and produces another sequence of words as output. It is often used for machine translation, text summarization, and dialogue systems.
"Large language models, as their most advanced form, are a combination of feedforward neural networks and transformers."
"They have superseded recurrent neural network-based models, which had previously superseded the pure statistical models, such as word n-gram language model."
"Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation, optical character recognition, handwriting recognition, grammar induction, information retrieval, and other."
"(helping prevent predictions of low-probability (e.g. nonsense) sequences)"
"Machine translation"
"generating more human-like text"
"optical character recognition"
"handwriting recognition"
"grammar induction"
"information retrieval"
"a combination of feedforward neural networks and transformers"
"recurrent neural network-based models"
"the pure statistical models, such as word n-gram language model"
"helping prevent predictions of low-probability (e.g. nonsense) sequences"
"speech recognition"
"generating more human-like text"
"grammar induction"
"information retrieval"
"a combination of feedforward neural networks and transformers"