#embeddings

#embeddings

Transformers are foundational to LLMs but have limitations in computational efficiency for long sequences, leading to the development of advanced models like BERT and SBERT.

Word2vec, proposed in 2013, revolutionized the use of word embeddings in language models.

Embeddings represent words as dense vectors that capture their meanings contextually.