A language model in NLP is a probabilistic framework designed to predict the likelihood of sequences of words in a language. It learns patterns, syntax, and semantics from large corpora of text to generate or analyze text. Language models can predict the next word in a sequence (e.g., "The cat sat on the ___") or evaluate the probability of a given sequence ("I am going home" vs. "I am home going").
Traditional language models, such as n-gram models, use statistical techniques to model text based on fixed-length word sequences. Modern language models, like BERT, GPT, and T5, use deep learning and transformer architectures to capture richer and more flexible representations of language. They leverage embeddings and attention mechanisms to handle context, enabling applications such as translation, summarization, and conversational AI.
Pre-trained language models have become the backbone of NLP, as they encode extensive linguistic knowledge and can be fine-tuned for specific tasks with relatively small datasets. Their ability to generalize across tasks has drastically reduced the time and resources required for building NLP applications.