LLMs
Transformer
A Transformer is a deep learning model architecture that adopts the mechanism of self-attention, differentially weighting the significance of each part of the input data. Primarily used in the field of natural language processing (NLP), it has become a foundational architecture for many state-of-the-art models.
Explanation
Transformers revolutionized NLP by addressing the limitations of recurrent neural networks (RNNs) like LSTMs and GRUs, which struggled with long-range dependencies in text. The key innovation is the self-attention mechanism, which allows the model to attend to different parts of the input sequence when processing each word, enabling it to capture relationships between words regardless of their distance. A Transformer consists of an encoder and a decoder, both typically composed of multiple identical layers. The encoder processes the input sequence, while the decoder generates the output sequence. Each layer contains multi-head self-attention and feed-forward neural networks. Positional encoding is added to the input embeddings to provide information about the position of words in the sequence, as the self-attention mechanism is permutation-invariant. Transformers can be pre-trained on massive datasets and then fine-tuned for specific tasks, a technique that has led to significant improvements in NLP performance. Their parallel processing capabilities also make them more efficient to train than RNNs.