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The Building Blocks of LLMs: Vectors, Tokens and Embeddings
- Large language models (LLMs) are revolutionizing the way machines understand, generate, and manipulate human language.
- The fundamental building blocks of LLMs are vectors, tokens, and embeddings, and they play a crucial role in the models' architecture.
- Vectors are the mathematical representation of words, phrases, or even entire documents.
- Tokens are the basic units of language that carry meaning and help in understanding the structure of text.
- Word embeddings are dense vector representations that map words to a continuous vector space, where semantically similar words are closer to each other.
- Pre-trained embeddings like Word2Vec and GloVe play a crucial role in enhancing the LLMs' performance.
- LLMs employ embeddings to tackle real-world NLP tasks like machine translation, sentiment analysis, text classification, and named entity recognition.
- Contextual embeddings and subword tokenization are recent innovations in the realm of NLP.
- Understanding vectors, tokens, and embeddings is crucial for anyone venturing into the world of NLP and LLMs.
- The field of NLP is constantly evolving, and new techniques and architectures are emerging to address the challenges and limitations of traditional approaches.
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