Decoder adds attention layer to refine encoder output in Transformers vs MoE
I was flipping through a paper on Transformers and the first thing that jumps out is the obvious split between encoder and decoder stacks. Both setups take an input sequence, run it through an encoder, then pass the result to a decoder that spits out tokens one by one. That hand-off is where things start to look different.
The encoder’s job, turning raw text into a dense vector, stays pretty much the same, but the decoder can be wired in a few ways depending on what the model is trying to do. In a plain Transformer, the decoder just reads the encoder’s output and its own previous guesses. MoE versions, on the other hand, tend to add an extra step that sifts through the encoded info more selectively.
It’s not entirely clear how that extra gating works in every case, but it seems to be the piece that can give one model an edge on certain tasks. Figuring out that mechanism is probably the key to understanding why performance can vary. The next part walks through what that step actually does inside the decoder pipeline.
The decoder uses these two parts as well, but it has an extra attention layer in between. That extra layer lets the decoder focus on the most relevant parts of the encoder output, similar to how attention worked in classic seq2seq models. If you want a detailed understanding of Transformers, you can check out this amazing article by Jay Alammar.
He explains everything about Transformers and self-attention in a clear and comprehensive way. He covers everything from basic to advanced concepts. Transformers work best when you need to capture relationships across a sequence and you have enough data or a strong pretrained model.
Most of the big names, ChatGPT, Gemini, Grok, still sit on a plain Transformer underneath. The new buzzword, Mixture of Experts (MoE), shows up and people start asking how different it really is. Some writers act as if MoE is a brand-new design; others just call a bigger Transformer an MoE.
The article doesn’t settle the debate, so it’s unclear whether MoE adds new mechanisms or simply scales what we already have. In the decoder, there’s an extra attention layer between the usual two parts, letting the model focus on the most relevant encoder outputs, pretty much the classic seq2seq attention trick. That detail hints that, despite the fresh label, the core operations stay recognizably Transformer-based.
It feels more like a naming shift than a structural overhaul, though the piece stops short of confirming that. I’d say we keep a healthy dose of skepticism about the MoE tag until the technical community spells it out more clearly.
Further Reading
- MoE vs Dense vs Hybrid LLM architectures - Wandb
- Mixture-of-Experts (MoE) LLMs - Cameron R. Wolfe, Ph.D. (Substack)
- Mixture-of-Experts (MoE) Architectures: 2024-2025 Literature Review - Rohan Paul
- The Big LLM Architecture Comparison - Ahead of AI - Ahead of AI (Sebastian Raschka)
- What is 'Mixture of Experts' in LLM Models? - Pinggy
Related Reading
LLMs & Generative AI OpenAI says AI saves knowledge workers 40‑80 minutes; use yields five‑fold gains
LLMs & Generative AI Grok Chat: AI for debugging, building, testing web apps with voice and images
LLMs & Generative AI Samsung adds Vision AI Companion, an AI Bixby, to TVs for real‑time queries
Common Questions Answered
What extra component does the decoder include when comparing a standard Transformer to a Mixture‑of‑Experts model?
The decoder adds an additional attention layer between its two main parts. This layer allows the decoder to focus on the most relevant sections of the encoder output, differentiating it from a plain Transformer decoder.
How does the decoder's extra attention layer function similarly to attention in classic seq2seq models?
The extra attention layer operates like the attention mechanism used in classic sequence‑to‑sequence models, by weighting encoder representations based on their relevance to the current decoding step. This lets the decoder attend selectively to encoder outputs, improving context alignment.
Which headline‑grabbing models are mentioned as sharing the same core Transformer architecture despite the MoE hype?
The article notes that models such as ChatGPT, Gemini, and Grok all rely on the same fundamental Transformer architecture. Even though they are often marketed with Mixture‑of‑Experts terminology, their core building blocks remain unchanged.
What key question remains unanswered about whether Mixture‑of‑Experts introduces fundamentally new mechanisms?
The piece leaves it unclear whether MoE brings truly novel mechanisms or simply represents a scaled‑up version of existing Transformers. Commentators are divided, with some treating MoE as a brand‑new design and others seeing it as a rebranding of larger Transformers.