3. Encoder-Decoder Architecture: Key Aspects and Internal Mechanism
In the realm of large language models, the encoder-decoder architecture serves as a fundamental framework. In this essay, we will explore the key aspects of this architecture, including an overview, training methodology, and the process of producing text from a trained model during serving time. Additionally, we will delve into the internal mechanisms of the encoder and decoder stages, discuss training techniques, and highlight the transition from traditional recurrent neural networks (RNNs) to transformer blocks in modern language models.
TABLE OF CONTENTS:
- TRAINING PHASE-> 3.1 TEACHER FORCING
- SERVING PHSE
- EVOLUTION OF ENCODER DECODER ARCHITECTURES
The encoder-decoder architecture is primarily a sequence-to-sequence model. It takes an input sequence, such as a sentence in English, and produces an output sequence, such as its translation in French.
This architecture functions as a machine that consumes sequences and generates sequences. For instance, it can be employed to create responses from large language models based on given prompts.
Thus, the encoder-decoder architecture serves as a versatile framework for sequence manipulation.
INTERNAL MECHANISM OF ENCODER-DECODER ARCHITECTURE
The encoder-decoder architecture typically comprises two stages: an encoder stage and a decoder stage.