What is the primary function of language models in Natural Language Processing (NLP)?

The primary function of language models in Natural Language Processing (NLP) is to predict the probability of a sequence of words. Language models are designed to understand the patterns and structures of a language by learning from vast amounts of text data. They estimate the likelihood of a given word or sequence of words following a specific context, allowing them to generate coherent and contextually appropriate text.

For instance, in tasks like text generation or autocomplete features, language models rely on this probability prediction to suggest the next word in a sentence based on the preceding words. This foundational capability underpins various applications of NLP, including speech recognition, language translation, and content generation, as it allows for the creation of human-like text that aligns with the linguistic rules learned during training.

Other functions listed, such as converting audio into text or translating text into different languages, are specific tasks that can leverage language models but are not their primary function. Similarly, measuring the complexity of sentences pertains more to syntactic analysis and not directly to the essence of what language models accomplish.