Understanding Encoder-Only, Decoder-Only, and Encoder–Decoder Models in Simple Terms

 When people talk about modern AI and language models, you’ll often hear terms like encoder-only, decoder-only, and encoder–decoder models. At first, these might sound technical or confusing, but the ideas are actually simple once you think about how we humans read, write, and translate language.

In this post, let’s break it down in a way that’s easy to follow—with simple explanations, examples, and analogies.

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1. Encoder-Only Models: The Readers

What they do:
Encoder-only models are designed mainly to understand text. They don’t try to generate long passages; instead, they read and build a meaningful internal representation of the text.

How they work (in simple terms):

• You give them some text.

• They process it to figure out patterns, context, and meaning.

• They turn that into a rich “understanding” that can be used for tasks like classification or finding relationships.

Best for:

• Sentiment analysis (positive or negative review?)

• Text classification (spam or not spam?)

• Named entity recognition (finding names, places, dates, etc.)

Examples: BERT, RoBERTa, DistilBERT.

Analogy: Imagine someone who reads a book carefully to understand what’s inside but doesn’t try to write anything themselves. They are excellent at comprehension.

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2. Decoder-Only Models: The Storytellers

What they do:
Decoder-only models are built to generate text. They predict the next word in a sentence, one step at a time, based on what they’ve seen so far.

How they work (in simple terms):

• Start with a prompt (few words or sentences).

• Predict the next word.

• Add that word to the text.

• Repeat the process until a full answer or story is built.

Best for:

• Chatbots and conversational AI

• Story writing and creative generation

• Code completion

• Autocomplete in search engines or text editors

Examples: GPT (like ChatGPT), LLaMA, Falcon.

Analogy: Think of a storyteller who, once given a starting line, keeps building the story one sentence at a time. They may not always deeply “understand” in the human sense, but they are excellent at carrying the narrative forward.

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3. Encoder–Decoder Models: The Translators

What they do:
Encoder–decoder models combine the best of both worlds: they understand the input (encoder) and then produce output (decoder). This makes them great for tasks where you need to transform one kind of text into another.

How they work (in simple terms):

• Encoder: Reads and understands the input text.

• Decoder: Uses that understanding to generate a new output.

Best for:

• Language translation (English → Tamil, French → Hindi)

• Summarization (long article → short summary)

• Question answering (read context → give precise answer)

Examples: T5, BART, MarianMT.

Analogy: Think of a translator. They read a book in English (encoder) and then rewrite it in Tamil (decoder). They aren’t just copying words; they’re transforming them into something new but equivalent.

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Putting It All Together

Here’s a quick comparison:

• Encoder-only = Understand text

• Decoder-only = Generate text

• Encoder–Decoder = Transform text (input → output)

These three types of models form the backbone of today’s AI applications, from simple classification tools to powerful chatbots and translation engines.

So, the next time you hear someone mention “encoder-only” or “decoder-only,” you’ll know:

• Encoders are readers,

• Decoders are storytellers,

• Encoder–decoders are translators.

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