LLMs - What are Language Models and What are Embedded Models

What are Language Models?

Language Models (LMs) are a type of artificial intelligence (AI) designed to understand, generate, and manipulate human language. They are trained on vast amounts of text data to predict the likelihood of a sequence of words, enabling them to perform tasks like text completion, translation, summarization, and question answering. Language models can be rule-based, statistical, or, more commonly today, based on deep learning architectures like Transformers. Examples of advanced language models include OpenAI's GPT (Generative Pre-trained Transformer) series, Google's BERT (Bidirectional Encoder Representations from Transformers), and others.

Language models are widely used in applications such as chatbots, virtual assistants, content creation, and more. They are capable of understanding context, grammar, and even nuances in language, making them highly versatile tools in natural language processing (NLP).

What are Embedded Models?

Embedded Models, in the context of NLP, refer to models that convert words, phrases, or sentences into numerical representations called "embeddings." These embeddings are dense vectors (arrays of numbers) that capture the semantic meaning of the text in a way that machines can process. The most common type of embedded model is the word embedding model, such as Word2Vec, GloVe, or FastText, which maps individual words to vectors in a high-dimensional space.

More advanced embedded models, like those based on Transformers (e.g., BERT, GPT), generate contextualized embeddings. These embeddings consider the surrounding words and context, providing a more nuanced representation of language. Embedded models are crucial for tasks like sentiment analysis, text classification, and information retrieval, as they enable machines to understand and compare the meaning of text.

In summary, while language models focus on generating or understanding text, embedded models focus on representing text in a numerical form that can be used for further analysis or machine learning tasks. Both are essential components of modern NLP systems.

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