In the context of GPT (Generative Pre-trained Transformer) models, the decoder plays a crucial role. Here's a detailed explanation:
* Decoder Function: The decoder in a GPT model is responsible for taking the input (often a sequence of text) and generating the appropriate output (such as a continuation of the text or an answer to a query).
* Architecture: GPT models are based on the transformer architecture, where the decoder consists of multiple layers of self-attention and feed-forward neural networks.
* Self-Attention Mechanism: This mechanism allows the model to weigh the importance of different words in the input sequence, enabling it to generate coherent and contextually relevant output.
* Generation Process: During generation, the decoder processes the input through these layers to produce the next word in the sequence, iteratively constructing the complete output.
* References:
* Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I.
(2017). Attention is All You Need. In Advances in Neural Information Processing Systems.
* Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training. OpenAI Blog.