Language Models: From N-grams to LLMs

Language model definition and purpose

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.

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Applications of language models

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