nyelvmodellezés

Nyelvmodellezés refers to the process of creating statistical models that can predict the probability of a sequence of words. These models are trained on vast amounts of text data and learn the statistical relationships between words, phrases, and sentences. The primary goal of language modeling is to understand and generate human language.

The output of a language model is a probability distribution over the vocabulary, indicating how likely each word is to appear next in a given sequence. This capability is fundamental to many natural language processing (NLP) tasks. For example, in speech recognition, language models help disambiguate between phonetically similar words by considering the likelihood of word sequences. In machine translation, they ensure that the translated output is grammatically correct and natural-sounding.

Modern language models, particularly neural language models, have achieved remarkable performance. These models, often based on architectures like recurrent neural networks (RNNs) and transformers, can capture complex long-range dependencies in text. Large language models (LLMs) are a prominent example, trained on internet-scale datasets and capable of performing a wide array of tasks, including text generation, question answering, summarization, and even coding. The development of language modeling continues to be a rapidly evolving field, pushing the boundaries of what machines can do with language.