Encoded-Decoded Architecture: The Backbone of Large Language Models

With the rise of Generative AI, including Google's latest advancements like?Vertex AI?and tools such as?Gen AI Studio, Model Garden, and Gen AI API, there's been a surge in interest and curiosity about how these technologies work. At the heart of many of these tools lies a fundamental concept i.e. the Encoder-decoder architecture.

I've recently completed a course offered by Google on Encoder-Decoder Architecture?and?gained a deeper understanding of the core mechanics behind these models. In this article, I’m excited to summarize what I learned from the course and share how this architecture powers a range of AI-driven applications today.

What is Encoder-Decoder Architecture?

At a high level, the encoder-decoder architecture is a sequence-to-sequence model. This means that it takes in a sequence of input data like a sentence or a prompt and generates an output sequence like a translation or a response. For example, consider a machine translation task where you input a sentence in one language, and the model outputs the corresponding sentence in another language.

For instance, if you input the sentence "She loves reading books," the model might output "Ella ama leer libros" in Spanish. Similarly, large language models (LLMs) use this architecture to take a prompt like "What is the capital of India?" and generate the appropriate response: “New Delhi.”

The Core Mechanics: Encoder and Decoder

• Encoder: The encoder's job is to take the input sequence whether it's a sentence in English or a prompt in a language model and process it into a vector representation i.e. a condensed form that captures the essence of the input. This vector holds all the relevant information needed for the model to understand the context and meaning of the input. It’s like translating the words in the input into a mathematical representation that the model can work with. The encoder could be built using various technologies, such as Recurrent Neural Networks (RNNs) or the more modern and powerful transformer blocks, which are commonly used in large language models today.
• Decoder: Once the encoder has processed the input and produced this vector, the decoder takes that vector and starts generating the output sequence. For example, in a translation task, it would generate the translated sentence word by word. The decoder operates by predicting the next word or token in the sequence based on what it has generated so far, combined with the context provided by the encoder’s vector. This process continues until the entire output sequence is generated.

In modern language models, the encoder-decoder architecture uses the transformer mechanism (introduced by Google), which enables the model to focus on important parts of the input sequence using a technique called attention. This attention mechanism helps the model focus on the most relevant information at each step of the generation process.

Training the Model: Data, Teacher Forcing, and Error Correction

Training an encoder-decoder model requires a dataset consisting of input-output pairs. For instance, in a translation task, this dataset would contain pairs of sentences: one in the source language like English, and one in the target language like Spanish. The model learns by comparing its output with the true target sentence and adjusting its internal parameters (or weights) to minimize the difference between its prediction and the true output. This is called Error correction or Back Propagation.

One key technique used in training these models is called Teacher forcing. In teacher forcing, the model is given the correct output at each step of the training process, rather than the output it generated itself. This approach helps the model learn to generate the correct sequence, even if it initially makes mistakes. By providing the correct token from the training set at each step, the model is encouraged to learn the correct sequence structure.

From Training to Real-World Use: Serving the Model

After training, the model is ready for serving, meaning it can be used to generate outputs based on new inputs. For example, if you provide a sentence or prompt, the trained model will process the input through the encoder-decoder pipeline and produce a response.

At this stage, the generation process begins. The model uses techniques like Beam search or Greedy search to decide which tokens to generate at each step. Greedy search picks the token with the highest probability, while beam search considers multiple possible outputs and selects the most likely one.

Another key part of the generation process is Temperature Scaling. By adjusting the temperature, we can control the creativity or determinism of the model’s output. A higher temperature allows the model to explore more varied and creative responses, while a lower temperature results in more deterministic, repetitive outputs. For example, when generating text, you might adjust the temperature to make the model more or less creative depending on the task.

Why Encoder-Decoder Architecture Matters ?

The encoder-decoder architecture is a foundational component of many AI applications, from language translation to content generation. By understanding the workings of this architecture from encoding and decoding sequences to using techniques like teacher forcing and beam search you gain insight into how large language models function at a fundamental level.

As AI technologies continue to evolve, the encoder-decoder model will remain a crucial part of this development, enabling more sophisticated applications that can generate, translate, and interact with human-like text in ever more creative and useful ways. Understanding this architecture is key to unlocking the potential of these technologies and staying at the forefront of AI innovation.