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Language models predict natural-language sequences by estimating the probability of the next word/token from prior context.
A language model is a computational model that predicts sequences in natural language. Its core purpose is to estimate the likelihood of the next word (or token) given prior context, enabling applications such as speech recognition, machine translation, natural language generation, optical character recognition, handwriting recognition, grammar induction, information retrieval, and even disaster response. Modern language models—especially large language models (LLMs)—are typically built using transformer architectures trained on very large text datasets (often including internet-scraped text). Earlier approaches included purely statistical models such as word n-gram models, which predict the next word from a fixed-size window of previous words. Over time, neural methods introduced continuous word representations (embeddings) to reduce issues like data sparsity, and transformers ultimately became the dominant approach for today’s most capable systems.
Language models predict natural-language sequences by estimating the probability of the next word/token from prior context.
They are used across many NLP and related tasks, including generation, translation, recognition, retrieval, and more.
Historically, n-gram statistical models used fixed context windows; neural models introduced embeddings to address sparsity; transformers and LLMs are now the most advanced approach.
A computational model that predicts sequences in natural language, typically by estimating the probability of the next word or token given previous context.
A statistical language model that predicts the next word using a fixed-size window of the previous n−1 words (e.g., bigram, trigram).
A technique used in statistical language models to avoid assigning zero probability to unseen n-grams in the training data.
A continuous vector representation of a word that encodes semantic meaning, helping reduce data sparsity compared with discrete representations.
A transformer-based AI model trained on vast amounts of text, commonly used for tasks like generation, summarization, translation, and analysis.
A neural network architecture that underlies most modern LLMs and is designed to model relationships across sequences effectively.
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