Understanding Encoder-Decoder Architectures

The transformer architecture has revolutionized the field of natural language processing, giving rise to a wide range of powerful language models. While the majority of these models fall under the category of generative Decoders, the core transformer architecture has actually yielded three distinct model architectures, each with its own unique capabilities and applications. Let's dive into these three transformer-based architectures - Encoders, Decoders, and Encoder-Decoders - exploring their inputs, outputs, example models, and the specific tasks they are best suited for. 🤖📚 Encoder: Input: A sequence of tokens (e.g., words, characters) Output: Contextualized representations of the input tokens Example Architectures: BERT, RoBERTa, DistilBERT Best Suited For: Natural language understanding tasks (e.g., question answering, text classification, named entity recognition) Decoder: Input: A sequence of tokens (e.g., words, characters) Output: A generated sequence of tokens (e.g., a translation, a summary) Example Architectures: GPT-X, Llama, Mistral Best Suited For: Natural language generation tasks (e.g., text generation, machine translation, summarization) Encoder-Decoder: Input: A sequence of tokens (e.g., words, characters) Output: A generated sequence of tokens (e.g., a translation, a summary) Example Architectures: Seq2Seq, T5, Bart Best Suited For: Sequence-to-sequence tasks (e.g., machine translation, text summarization, dialogue generation) S2 Labs #LanguageModels #NaturalLanguageProcessing #TransformerArchitecture

As a technology enthusiast working on products and platform initiatives, I've spent the past seven+ months diving deep into the world of Large Language Models (LLMs) and sharing this journey with others. Today, I want to share a simple yet effective analogy illustrating how an LLM works. Through a Culinary Analogy 🍕🤖 - Demystifying Large Language Models (LLMs): Imagine you're in a kitchen, and you ask, "What's the best way to make a pizza?" This is where the magic of LLMs begins. Step 1: The Encoder - Understanding Your Request The encoder acts as the attentive listener in our kitchen. It analyzes your question layer by layer, understanding key terms like "best," "make," and "pizza." It's not just about the words; it's about grasping the intent behind them – in this case, you're seeking a recipe or cooking tips. Step 2: The Decoder - Crafting the Response Next, we move to the decoder - the creative chef of our digital kitchen. Armed with the understanding provided by the encoder, it begins to craft a response. It sifts through various pizza recipes, considers popular tastes, and even sprinkles in some cooking advice. The result? A well-constructed reply that might offer you a step-by-step guide to making the perfect pizza, tailored to be informative and easy to understand. In essence, the encoder and decoder in an LLM work in unison, like a well-oiled team in a bustling kitchen. The encoder reads and understands the order while the decoder prepares the dish, ensuring that the response is not just accurate but also clear, coherent, and practical. Through this analogy, I hope to bring the complex workings of LLMs closer in a relatable manner. It's fascinating how technology, much like a skilled chef, can transform simple ingredients (data) into something delightful (useful and coherent responses). This is an oversimplified version; I will share another learning with additional components of the architecture: Embeddings, FFNs, and more in the next post. #llms #ai #innovation #TechnologyExplained

LLM Architectures, Demystified Understanding how large language models work should not require a PhD. So check out this clear, visual breakdown of the 6 core LLM architectures that power today’s most advanced AI systems. Whether you’re building, investing, or just curious about the models behind the AI revolution, this will give you a solid mental map. 🔍 What you’ll learn in the carousel: 🈸Encoder-Only: Ideal for language understanding tasks like classification and sentiment analysis. Think BERT and RoBERTa. 🈴Decoder-Only: The foundation of autoregressive models like GPT, optimized for text generation. 💹Encoder-Decoder: A flexible architecture behind models like T5 and BART, perfect for translation, summarization, and question answering. 🛗Mixture of Experts (MoE): Used in models like Mixtral, this architecture activates only a subset of the model’s parameters at inference, offering scale with efficiency. ♐️State Space Models (SSM): Architectures like Mamba enable fast inference and long context retention, moving beyond attention bottlenecks. 🔀Hybrid Architectures: Combinations like Jamba bring together transformers, state space models, and MoE to capture the best of each approach. Hope that builders, product leaders, or AI enthusiasts can use this guide to understand what’s happening under the hood. 👉 Swipe through the carousel 🔁 Share with someone trying to grasp LLM fundamentals 💬 Let me know which architecture you find most promising #llm #aiagents #artificialintelligence

The term (large) language model can refer to many types of models beyond GPT-style, generative language models. Here are the three primary types of language models and the tasks they are best suited for… TL;DR: Three primary types of language models exist, distinguished by the type of transformer architecture that they use. The current (GPT-style) generative LLMs use a decoder-only architecture, which is specialized for text generation via next token prediction. The transformer architecture. Most modern LLMs use a transformer architecture. In its original form, this architecture contains both an encoder and a decoder. However, variants of the transformer exist the selectively use the encoder/decoder in isolation, producing three primary variants of the transformer architecture: 1. Encoder-decoder (e.g., T5) 2. Encoder-only (e.g., BERT) 3. Decoder-only (e.g., GPT) Encoder-decoder architecture. The encoder-decoder architecture was originally proposed for Seq2Seq tasks (e.g., language translation or summarization). However, the text-to-text transformer (T5) demonstrated that this architecture works well in the transfer learning domain, especially on language modeling tasks with a clear prefix (due to the use of an explicit encoder). Encoder-only models. The encoder-only architecture is heavily utilized for transfer learning on discriminative, language-based tasks (e.g., classification, tagging, and QA). See BERT as a notable example. We cannot use it for any tasks that requires text generation due to the lack of a decoder. However, encoder-only models are heavily used for retrieval/search applications (e.g., sBERT). Decoder-only models. The decoder-only (GPT-style) architecture is the workhorse of modern, generative language models. This architecture is perfect for efficient training and inference via next token prediction. Plus, its generic, text-to-text format enables us to solve a variety of problems by formulating them as a prompt-and-response pair.