# Could someone explain the difference between encoder-only and encoder-decoder models in the context of question answering?

**URL:** <https://discuss.huggingface.co/t/could-someone-explain-the-difference-between-encoder-only-and-encoder-decoder-models-in-the-context-of-question-answering/169360>\
**Category:** Beginners\
**Created:** [October 23, 2025, 6:41am UTC](https://discuss.huggingface.co/t/could-someone-explain-the-difference-between-encoder-only-and-encoder-decoder-models-in-the-context-of-question-answering/169360 "2025-10-23T06:41:49Z")\
**Posts on this page:** 4\
**Page:** 1

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**Author:** ![Suhebmultani](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/suhebmultani/32/49771_2.png) [@Suhebmultani](https://discuss.huggingface.co/u/Suhebmultani)\
**Post date:** [October 23, 2025, 6:41am UTC](https://discuss.huggingface.co/t/could-someone-explain-the-difference-between-encoder-only-and-encoder-decoder-models-in-the-context-of-question-answering/169360/1 "2025-10-23T06:41:49Z")

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What is the difference between two types of transformer models (encoder-only vs encoder-decoder) and how they are used for answering questions?

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**Author:** ![mahmutc](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/mahmutc/32/52583_2.png) [@mahmutc](https://discuss.huggingface.co/u/mahmutc)\
**Post date:** [October 23, 2025, 8:26am UTC](https://discuss.huggingface.co/t/could-someone-explain-the-difference-between-encoder-only-and-encoder-decoder-models-in-the-context-of-question-answering/169360/2 "2025-10-23T08:26:58Z")

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Encoder-only models are well-suited for extractive question answering tasks, where the model identifies the most relevant span of text as the answer.  
Encoder-decoder models are better for open-ended questions like “Why is the sky blue?” — they can synthesize information and produce a natural, explanatory answer. The encoder builds a representation of the input, and the decoder generates new content based on that representation.

For more information, check out:

> **[Transformer Architectures - Hugging Face LLM Course](https://huggingface.co/learn/llm-course/en/chapter1/6)**
>
> We’re on a journey to advance and democratize artificial intelligence through open source and open science.

> **[Question answering - Hugging Face LLM Course](https://huggingface.co/learn/llm-course/en/chapter7/7)**
>
> We’re on a journey to advance and democratize artificial intelligence through open source and open science.

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**Author:** ![datadata123](https://avatars.discourse-cdn.com/v4/letter/d/3ab097/32.png) [@datadata123](https://discuss.huggingface.co/u/datadata123)\
**Post date:** [October 23, 2025, 1:58pm UTC](https://discuss.huggingface.co/t/could-someone-explain-the-difference-between-encoder-only-and-encoder-decoder-models-in-the-context-of-question-answering/169360/3 "2025-10-23T13:58:44Z")

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The encoder-only transformer model processes the input from left to right and from right to left, so they know contexy around the word. BERT, RoBERTa are models like this. For question answering the might be used for extractive question answering, eg. we heave context: “…In 1928, Alexander Fleming discovered penicillin…” and question: “Who discovered penicillin?”.

For encoder-decoder encoder reads the input and then the decoder produces output token by token. It is better for answering generative questions, so it can handle more abstract QA, for example summarize, rephrase informations.

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**Author:** ![amarawallis](https://avatars.discourse-cdn.com/v4/letter/a/96bed5/32.png) [@amarawallis](https://discuss.huggingface.co/u/amarawallis)\
**Post date:** [October 24, 2025, 10:52am UTC](https://discuss.huggingface.co/t/could-someone-explain-the-difference-between-encoder-only-and-encoder-decoder-models-in-the-context-of-question-answering/169360/4 "2025-10-24T10:52:58Z")

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Great explanations above! Just to expand, in practice, if your dataset has _context + question + answer span_, encoder-only models (like BERT QA) work best.  
But if you’re building a chatbot or need more _natural, free-form_ answers, encoder-decoder (T5/BART) or even decoder-only (GPT) models are better suited.