Language models play a significant role in NLP by serving as probability distributions over sequences of words or terms. They enable a range of applications like machine translation, sentiment analysis, language generation, and speech recognition. These models learn from vast amounts of text data to predict the likelihood of a sequence, allowing them to generate coherent and contextually appropriate sentences or predict the next word in a given context.

Language models are widely used in NLP to capture the probability distribution of sequences of words or terms. By understanding the context and relationships between words, these models can be employed for tasks such as autocompletion in search engines, speech recognition systems, and even chatbots. They rely on statistical techniques to estimate the likelihood of word sequences, enabling them to generate sensible and contextually appropriate language.

In the realm of NLP, language models are crucial as they represent the probability distribution over sequences of words or terms. These models are trained on large datasets and learn the patterns and relationships between words. This understanding enables them to generate coherent and contextually appropriate language. They have applications in machine translation, text summarization, and speech recognition. By leveraging language models, NLP algorithms can provide more accurate and sophisticated results.