Understanding the attention mechanism
Following the challenges presented by the fixed-length memory in traditional Seq2Seq models, 2014 marked a revolutionary step forward. Dzmitry Bahdanau, KyungHyun Cho, and Yoshua Bengio proposed a transformative solution: the attention mechanism. Unlike earlier models that tried (often in vain) to condense entire sequences into limited memory spaces, attention mechanisms enabled models to hone in on specific, relevant parts of the input sequence. Picture it as a magnifying glass over only the most critical data at each decoding step.
What is attention in neural networks?
Attention, as the adage goes, is where focus goes. In the realm of NLP and particularly in the training of LLMs, attention has garnered significant emphasis. Traditionally, neural networks processed input data in a fixed sequence, potentially missing out on the relevance of context. Enter attention—a mechanism that weighs the importance of different input data,...
