Understanding Encoder-Decoder Architectures

Large Language Models (LLMs) may look similar on the surface, but their architectures define their strengths, trade-offs, and use cases. Understanding these differences is key to making the right choices in research and real-world applications. Here’s a deeper look at the four foundational LLM architectures 1. Decoder-Only Models (GPT, LLaMA) -Autoregressive design: predict the next token step by step. -Powering generative applications like chatbots, assistants, and content creation. Strength: fluent, creative text generation. Limitation: struggles with tasks requiring bidirectional context understanding. 2. Encoder-Only Models (BERT, RoBERTa) -Built to understand rather than generate. -Capture deep contextual meaning using bidirectional self-attention. -Perfect for classification, search relevance, and embeddings. Strength: strong semantic understanding. Limitation: cannot generate coherent long-form text. 3. Encoder–Decoder Models (T5, BART) -Combine the understanding power of encoders with the generative power of decoders. -Suited for sequence-to-sequence tasks: summarization, translation, Q&A. Strength: flexible and powerful across diverse NLP tasks. Limitation: computationally more expensive compared to single-stack models. 4. Mixture of Experts (MoE: Mixtral, GLaM) -Leverages a gating network to activate only a subset of parameters (experts) per input. -Provides scalability without proportional compute cost. Strength: massive capacity + efficiency. Limitation: complexity in training, routing, and stability. Decoder-only models dominate today’s consumer AI (e.g., ChatGPT), but MoE architectures hint at the future — scaling models efficiently without exploding costs. Encoder-only and encoder–decoder models remain critical in enterprise AI pipelines where accuracy, context understanding, and structured outputs matter more than freeform generation. The next decade of AI may not be about “bigger is better,” but about choosing the right architecture for the right job — balancing efficiency, accuracy, and scalability. Which architecture do you believe will shape enterprise AI adoption at scale — GPT-style generalists or MoE-driven specialists?

Johns Hopkins & LightOn Release ETTIN - The First True Apples-to-Apples Comparison Between Encoder and Decoder Language Models The language model community has long debated encoder vs decoder architectures, but fair comparisons were impossible due to different training data, architectures, and recipes. That changes today. What Makes ETTIN Different: - 10 paired models (5 encoder-decoder pairs) from 17M to 1B parameters - Identical training recipe, data, and architecture - only differing in attention patterns (bidirectional vs causal) and objectives (MLM vs CLM) - Trained on 2 trillion tokens using a three-phase approach: base pre-training (1.7T tokens), mid-training with context extension to 8K tokens, and decay phase with high-quality data Technical Architecture Deep Dive: - Uses RoPE attention with 160K base parameters for long context - Deep-but-thin design inspired by MobileLLM principles - 30% masking ratio for encoders (reduced to 15% in decay phase) - Trapezoidal learning rate scheduling across all phases - Global attention every 3 layers with 128-token sliding window Key Findings: - Cross-objective training fails: Even after 50B additional tokens, a 400M encoder outperforms a 1B decoder on MNLI classification - Architecture-specific strengths persist: Encoders dominate retrieval/classification (91.3% MNLI accuracy), decoders excel at generation (59.0 average score) - Both achieve SOTA performance in their respective domains - beating ModernBERT and Llama 3.2/SmolLM2 Under the Hood: The bidirectional attention in encoders allows full context awareness during masked language modeling, optimizing for understanding relationships. Decoders use causal masking during autoregressive training, building sequential generation capabilities. The researchers discovered that these fundamental differences in attention patterns create irreversible architectural specializations. This work provides the foundation for fair encoder-decoder comparisons and reveals why specialized architectures still matter in the age of large language models. All models, training data, and 200+ checkpoints are open-sourced for the research community.

Everyone prepares for LLM interviews by memorising terms. Strong candidates understand the system well enough to explain how everything fits together. So I put together a clear interview guide that breaks down the core concepts behind Transformers and modern LLMs in a way you can actually use in technical conversations. Here’s what this framework helps you explain confidently 👇 1.Why Transformers replaced RNNs ↳ Parallel attention instead of sequential processing ↳ Better handling of long-range dependencies 2.How data flows through the model ↳ Embeddings, positional encodings, attention layers, FFNs, projection heads ↳ What each component adds to the final representation 3.How attention really works ↳ Queries, Keys, Values ↳ Multi-head attention and why multiple heads matter 4.Encoder, Decoder, and Encoder–Decoder models ↳ When each architecture is used ↳ How LLMs like GPT differ from BERT 5.What modern improvements solve ↳ RoPE for long context ↳ MoE for scale ↳ Optimised attention patterns for efficiency 6.Common failure modes you should know ↳ Hallucinations ↳ Repetition loops ↳ Context loss ↳ Tokenisation mismatches 7.How to debug Transformer behaviour in interviews ↳ Inspect attention maps ↳ Validate embedding similarity ↳ Compare sampling methods ↳ Analyse cross-attention influence This is the level of clarity interviewers want. Not buzzwords. Not half answers. A real understanding of how LLMs work under the hood. If you want, I can also turn this into a one-page cheat sheet, a carousel, or a mock interview script. ♻️ Repost it to help someone preparing for their next AI role. ➕ Follow Naresh Edagotti for more content that makes complex AI topics feel simple.

This blog post is a hands-on, beginner-friendly guide to building a Transformer model from scratch using PyTorch. It covers both theoretical foundations and practical implementation, making complex concepts accessible. The blog provides some good context: ► Transformer Basics: Introduction to self-attention, positional encoding, and encoder-decoder structure. ► Deep Dive into Theory: Explains key concepts from the "Attention is All You Need" paper, including query, key, and value in self-attention. ► PyTorch Implementation: Step-by-step coding of multi-head attention, feed-forward layers, normalization, and training setup. ► Practical Insights: Tips for optimizing training, relevant resources, and independent learning encouragement. ► Context & Comparison: Discusses why Transformers outperform RNNs and their original application in machine translation. Link: https://lnkd.in/gAqjWKsu

So... people asked what was on my shirt from Monday's post. It's the diagram that runs most of the AI you use every day. No big deal... It's the transformer architecture. The "T" in GPT. It's really good at figuring out which words matter to which other words. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Input Embedding: Converts text to numbers. Computers can't read English, so words become vectors (lists of numbers). Positional Encoding: The model needs to know "dog bites man" differs from "man bites dog," so it adds position data. Order matters. The Encoder (left side): Reads your input through N layers (usually 6-12) with three operations: • Multi-Head Attention: Asks "which words matter?" For "it" in a sentence, attention figures out what "it" refers to. Multi-head means doing this in parallel, looking for different patterns. • Feed Forward: A simple neural network that processes each word independently after understanding context. • Add & Norm: Keeps the model stable during training. Boring but necessary. The Decoder (right side): Generates output one token at a time with the same components, plus: • Masked Multi-Head Attention: When generating word 5, you can only see words 1-4. No cheating. Then it combines what the encoder understood with what it's already generated. • Linear + Softmax: Converts output into probabilities for every possible next word. Pick the most likely. Repeat. This is where the probabilistic nondeterminism happens (and the bane of our existence). 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝘄𝗼𝗿𝗸𝘀 Attention. Previous models processed text sequentially. Transformers look at everything at once and learn which parts matter. This lets them run in parallel (faster), handle long-range dependencies (connect ideas across paragraphs), and scale up. The stacking lets it learn abstract patterns. Layer 1 learns grammar. Layer 12 learns reasoning. 𝗪𝗵𝗮𝘁'𝘀 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 Training details, tokenization, and attention masks. But this is the core. GPT uses just the decoder. BERT uses just the encoder. Most large language models today are decoder-only because generating text is the valuable task. The paper that started all of this? "Attention is All You Need" on ArXiv. That's what's on my shirt. Questions? Ask. #AI #GPT #Nerd

Check out this simple breakdown of the four core LLM architectures shaping most of today’s advanced systems. Not all LLMs reason the same way. Building reliable AI systems, or choosing the right model for a workflow requires a clear mental model of the architectures that drive them. Let’s review: 1.🔸Decoder Only (GPT, LLaMA, Grok) Generates text by predicting one token at a time using previous context. Best for generative workloads, conversational systems, and multi step reasoning. 2.🔸Encoder Only (BERT, RoBERTa) Processes text with full bidirectional context to learn dense semantic representations. Best for classification tasks, retrieval, and embedding generation. 3.🔸Encoder Decoder (T5, FLAN T5, BART) Separates understanding from generation. The encoder builds meaning, the decoder transforms it. Best for translation, summarization, and any structured text to text transformation. 4.🔸Mixture of Experts (Mixtral, Gemini 1.5) Routes each input to a subset of specialized expert networks. Best for scaling model capacity while controlling compute cost. These architectures go beyond academic details to determine latency profiles, context handling, fine tuning strategies, and how models behave under real workloads. A clear grasp of them helps you design better pipelines, choose the right foundation models, and understand why two LLMs can produce very different outcomes on the same task. Let me know your thoughts! #LLM

Not all LLMs generate text. Most people seem to forget that. Architectures and training methods vary widely between LLMs (and SLMs). But what most modern language models have in common is that they are based on the transformer architecture. And when it comes to pre-training modern LLMs, there are two key architectures that stand out: encoder-based models and decoder-based models. They’re each good at different types of tasks and that’s because they are trained differently. ⭐️ Encoder-based models focus on language understanding and generally tend to be smaller. These models are trained on Masked Language Modeling (MLM) tasks. Here’s how it works: 1. A percentage of the input tokens in the sequence are randomly blanked out. The encoder processes the corrupted sequence, generating contextual embeddings for every token, including the masked ones. 2. The model must predict the original, masked tokens based only on the surrounding context provided by the unmasked tokens and the generated embeddings. 3. Cross-Entropy Loss is calculated by measuring the dissimilarity between the model’s predicted word probability and the true original word. ➪ The model is trained to minimize the loss function, learning a bidirectional understanding of the text (because it can use the tokens in front of AND after the masked token to predict what the masked token should be). ⭐️  Decoder-based models are focused on language generation and are definitely on the larger side (think billions to trillions of parameters). These models are trained on Causal Language Modeling (CLM) tasks. Here’s how it works: 1. The model is fed a sequence of tokens from the training text, and the attention mechanism is restricted so that each token can only look backward at preceding tokens. 2. The model is trained to perform Next Token Prediction: predicting the most probable next token in the sequence, using only the preceding tokens to guide it. 3. Cross-Entropy Loss is calculated for each prediction against the true subsequent token. ➪ The process trains the decoder to develop an autoregressive, unidirectional understanding essential for sequential text generation. Learning how a model is trained is imperative to understanding which tasks it’s best suited to complete. These two blog posts on encoder vs. decoder models are a great place to start. Encoder vs. Decoder vs. Encoder-Decoder Models [Aman Chadha] 🔗: https://lnkd.in/g5bm3hhc Understanding Encoder And Decoder LLMs [Sebastian Raschka] 🔗: https://lnkd.in/gjkvXi68

Transformer Model: The Architecture Behind AI Virtually every advanced AI system from GPT-4 to Claude are built upon Transformer architecture. Introduced in the landmark 2017 paper "Attention Is All You Need," Transformers made quantum leap on how AI processes sequential data by replacing traditional recurrent networks with something more powerful: attention mechanisms. Look closely, you'll see the two core components: 🔍 ENCODER The encoder transforms input data into hidden representations by capturing relevant features and dependencies. Unlike previous architectures, Transformers process entire sequences simultaneously rather than one token at a time. 🔄 DECODER The decoder takes these representations and generates outputs like translations or predictions based on the encoded information. The attention mechanism in those multi-head attention blocks allow the model to focus on different parts of the input simultaneously, weighing the relevance of each word in relation to others regardless of their distance from each other in the sequence. Each layer contains: 1️⃣ Add & Norm: Residual connections and layer normalization for stable training 2️⃣ Feed Forward: Neural networks that transform the attention outputs 3️⃣ Multi-Head Attention: The disruptive component that enables parallel processing The positional embeddings shown at the bottom provide crucial sequence order information, replacing the inherent sequentiality of recurrent models. This architecture is why we're seeing such remarkable advances in AI capabilities such as language understanding, code generation and beyond. Over to you: What are the limitations of Transformers then?

The Most Comprehensive Notes on Transformers by Carnegie Mellon University Part 1: Transformers - Encoder & Decoder Architectures - Self-Attention and Multi-Head Attention (visually explained) - Query, Key & Value with real-world analogies - Positional Encoding (incl. Rotary Embeddings) - Masked Attention & Encoder-Decoder Attention - End-to-End Translation Example: "I ate an apple" → "Ich habe einen Apfel gegessen" Part 2: The LLM Revolution - BERT: Bidirectional Understanding with Masked LM & NSP - GPT: Pretraining via Causal LM and Prompt-Based Fine-Tuning - Why LLMs changed everything: From feature engineering to zero-shot reasoning - Full timeline: From GPT → GPT-4, BERT → DeBERTa → T5, and more Transformers aren't just another model; they're a paradigm shift, and these notes break it down better than most courses.

6 Core LLM Architectures Every AI Builder Should Know (2025 Edition) Not all LLMs are created equal. And if you don’t know 𝘩𝘰𝘸 they’re built, you’ll never know 𝘸𝘩𝘦𝘯 to use them. → Some models are great at understanding text → Some are optimised for generating long outputs → Some scale better with lower compute Here are the 𝟲 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗟𝗟𝗠 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 to know in 2025 𝟭. 𝗘𝗻𝗰𝗼𝗱𝗲𝗿-𝗢𝗻𝗹𝘆 (𝗔𝘂𝘁𝗼𝗲𝗻𝗰𝗼𝗱𝗲𝗿𝘀) 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Uses a bidirectional transformer encoder to understand the full context of input text 𝗧𝗿𝗮𝗶𝗻𝗲𝗱 𝘄𝗶𝘁𝗵: Masked Language Modelling (MLM) — randomly hide words and predict them 𝗚𝗿𝗲𝗮𝘁 𝗳𝗼𝗿: Text understanding, embeddings, classification 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: BERT, RoBERTa 𝟮. 𝗗𝗲𝗰𝗼𝗱𝗲𝗿-𝗢𝗻𝗹𝘆 (𝗔𝘂𝘁𝗼𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝘃𝗲) 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Uses a unidirectional decoder to predict the next token in a sequence 𝗧𝗿𝗮𝗶𝗻𝗲𝗱 𝘄𝗶𝘁𝗵: Causal Language Modelling (CLM) — predict next word given previous ones 𝗚𝗿𝗲𝗮𝘁 𝗳𝗼𝗿: Text generation, few-shot prompting, agents 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: GPT-4, LLaMA 3, Claude 𝟯. 𝗘𝗻𝗰𝗼𝗱𝗲𝗿-𝗗𝗲𝗰𝗼𝗱𝗲𝗿 (𝗦𝗲𝗾𝟮𝗦𝗲𝗾) 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Encodes the input, then decodes a response, like translating one sentence to another 𝗧𝗿𝗮𝗶𝗻𝗲𝗱 𝘄𝗶𝘁𝗵: Span corruption or sequence-to-sequence objectives 𝗚𝗿𝗲𝗮𝘁 𝗳𝗼𝗿: Translation, summarisation, input-output tasks 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: T5, BART 𝟰. 𝗠𝗶𝘅𝘁𝘂𝗿𝗲 𝗼𝗳 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 (𝗠𝗼𝗘) 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Only a few specialised “experts” activate for each input, reducing total compute 𝗧𝗿𝗮𝗶𝗻𝗲𝗱 𝘄𝗶𝘁𝗵: Gating networks that route inputs to specific sub-models 𝗚𝗿𝗲𝗮𝘁 𝗳𝗼𝗿: Scaling large models efficiently 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: DeepSeek-V2, LLaMA 4 𝟱. 𝗦𝘁𝗮𝘁𝗲 𝗦𝗽𝗮𝗰𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗦𝗦𝗠) 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Replaces attention with state-based transitions — processes sequences linearly in time 𝗧𝗿𝗮𝗶𝗻𝗲𝗱 𝘄𝗶𝘁𝗵: State-space dynamics rather than token-by-token attention 𝗚𝗿𝗲𝗮𝘁 𝗳𝗼𝗿: Long documents, faster inference, memory efficiency 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Mamba 𝟲. 𝗛𝘆𝗯𝗿𝗶𝗱 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Combines components from multiple architectures — e.g., Transformers + SSMs 𝗧𝗿𝗮𝗶𝗻𝗲𝗱 𝘄𝗶𝘁𝗵: Mixed objectives depending on layers/modules 𝗚𝗿𝗲𝗮𝘁 𝗳𝗼𝗿: Balancing speed, scale, and accuracy 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Jamba (Transformer + Mamba hybrid) Each architecture solves a different problem. Knowing which one you’re working with helps you: ✔️ Choose better models ✔️ Design smarter systems ✔️ Avoid expensive mistakes If you’re serious about building with LLMs, 𝘀𝗮𝘃𝗲 𝘁𝗵𝗶𝘀 𝗳𝗼𝗿 𝗹𝗮𝘁𝗲𝗿. ♻️ Repost to share with your network. And for deeper breakdowns every week: NeoSage https://blog.neosage.io  — My free newsletter for engineers building with AI.