Encoder vs Decoder in LLMs: A Beginner’s Guide to Understanding Transformer Models

2026-06-13 · Source: NLP on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Novice, quick

Summary

This guide clarifies the fundamental distinctions between encoder-only, decoder-only, and encoder-decoder Transformer architectures, which are crucial for understanding Large Language Models (LLMs). Encoder-only models, like BERT and RoBERTa, excel at understanding entire text inputs for tasks such as sentiment analysis, classification, and information retrieval. Decoder-only models, including GPT, Llama, and Gemma, generate text sequentially, predicting one token at a time based only on past context, making them ideal for chatbots, text generation, and story writing. Encoder-decoder models, such as T5 and FLAN-T5, first understand an input with an encoder and then generate a transformed output using a decoder, best suited for translation, summarization, and question answering.

Key takeaway

For AI Engineers selecting an LLM architecture, understanding the core function of encoder-only, decoder-only, and encoder-decoder models is crucial. Your choice directly impacts task suitability; use encoder-only for text understanding, decoder-only for generation, and encoder-decoder for transformation. Aligning the model type with your specific Natural Language Processing task optimizes performance and resource allocation.

Key insights

Transformer architectures are specialized: encoders understand, decoders generate, and encoder-decoders transform text.

Principles

• Encoders process entire inputs for deep understanding.
• Decoders generate text sequentially, seeing only past tokens.
• Encoder-decoders combine understanding with generative transformation.

In practice

• Use BERT for sentiment analysis or classification.
• Employ GPT for chatbots and text generation.
• Apply T5 for translation and summarization tasks.

Topics

• Large Language Models
• Transformer Architectures
• Encoder Models
• Decoder Models
• Encoder-Decoder Models
• Natural Language Processing
• Text Generation

Best for: AI Student, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by NLP on Medium.