LLM - Transformer Architecture - Decoders and Encoders

In the realm of machine learning, particularly in natural language processing, the transformer architecture has revolutionized how we approach tasks such as translation, summarization, and text generation. At the heart of this architecture are two main components: encoders and decoders. Understanding their roles is crucial for grasping how large language models (LLMs) function.

The encoder's primary responsibility is to process input data, transforming it into a format that captures the underlying meaning of the text. It does this through a series of self-attention mechanisms and feed-forward neural networks. The self-attention mechanism allows the model to weigh the importance of different words in relation to one another, effectively capturing context and relationships within a sentence or phrase. This enables the encoder to create a rich representation of the input sequence that retains essential semantic information.

On the other hand, decoders take these encoded representations and generate output sequences based on them. While they also utilize self-attention mechanisms, they include an additional layer that attends not only to previous outputs but also to the encoder's output. This ensures that each word generated by the decoder is informed by both what has been produced so far and what was learned from the input data. As a result, decoders are capable of producing coherent and contextually relevant sentences or paragraphs.

In summary, both encoders and decoders play critical roles in transformer architectures. Encoders focus on understanding and representing input data while decoders specialize in generating meaningful output from these representations. This interplay allows large language models to perform complex language tasks with impressive accuracy and fluency.