"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."
Predicting the probability of a given sequence of words occurring in a text.
Corpus Linguistics: The study of language data collections, used in the development of models.
Probability Theory: A mathematical framework for measuring the likelihood of an event in language models.
Bayesian Inference: A statistical method that allows for the estimation of probabilities of events.
Markov Models: A type of probability model that assumes that the probability of a word or phrase depends only on the preceding words.
N-grams: A sequence of N words that are used to model language in statistical language models.
Hidden Markov Models: A statistical model that represents the probability distribution over sequences of observed output.
Recurrent Neural Networks: A type of neural network used in language modeling that allows for the prediction of sequential data.
Word Embeddings: A method of representing words in a lower-dimensional vector space that captures the meaning of the words.
Transformer Models: A deep learning architecture that has transformed natural language processing, leading to major breakthroughs in language modeling.
Self-supervised Learning: A method of training a model by using information from the data to generate labels.
Transfer Learning: A technique that allows a model to reuse knowledge learned from one task to improve performance on another task.
Preprocessing: Transforming text into an appropriate format for modeling.
Model Evaluation: Assessing the quality of a language model, by measuring its accuracy and efficiency.
Error Analysis: A technique used to analyze the mistakes made by a language model, with the aim of improving its performance.
Human-Computer Interaction: A research area that focuses on the design and evaluation of systems that support natural language interaction between humans and machines.
Applications of Language Models: The use of language models in various disciplines such as speech recognition, machine translation, sentiment analysis, and others.
N-gram Language Modeling: This model predicts the likelihood of a word or phrase based on its previous n-1 words or phrases.
Neural Language Modeling: This model uses neural networks to predict the next word or phrase in a sentence based on the context.
Rule-based Language Modeling: This model uses a set of rules to generate sentences based on a predefined grammar.
Knowledge-based Language Modeling: This model uses knowledge about the world to generate coherent sentences and discourse.
Statistical Language Modeling: This model uses statistical methods to predict the likelihood of a word or phrase based on its frequency in a given corpus.
Probabilistic Language Modeling: This model uses probability theory to generate sentences based on the probability of a word or phrase occurring in a given context.
Context-based Language Modeling: This model considers the entire context of a sentence or conversation to generate responses.
Hierarchical Language Modeling: This model represents language at multiple levels of abstraction, from individual words to whole sentences, to generate more complex discourse.
Syntactic Language Modeling: This model uses syntactic structures to generate grammatically correct sentences.
Semantic Language Modeling: This model uses semantic structures to generate meaningful and coherent sentences.
Machine Translation Language Modeling: This model translates one language to another by mapping words and phrases based on their context and meaning.
"They 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."
"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."
"Generating more human-like text."
"Optical character recognition."
"To recognize handwritten text."
"By identifying and establishing grammatical patterns in text."
"Information retrieval."
"Generating probabilities of a series of words."
"They are combined to form large language models."
"They have superseded recurrent neural network-based models."
"They had previously superseded the pure statistical models."
"Generating more human-like text."
"Predictions of low-probability (e.g. nonsense) sequences."
"Optical character recognition, handwriting recognition, and grammar induction."
"They can aid in retrieving relevant information."
"They have superseded recurrent neural network-based models and pure statistical models."