Snippet Name: Decoder_Only_Language_Models

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Created Date: 2025-06-16 13:12:39
Last Modified Date: 2025-05-31 10:31:48

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Title: Decoder Only Language Models
Author: user
when we are talking about large language models, what is a decoder only language?

Author: ChatGPT
A **decoder-only language model** refers to a type of architecture used in large language models (LLMs) where only the decoder part of the transformer model is used. In the transformer architecture, there are two main components: an **encoder** and a **decoder**. Here's how the **decoder-only** model works:

- **Architecture**: It consists solely of the decoder, which is designed to generate sequences of text (or tokens) by predicting the next word based on the previously generated words.
- **Input and Output**: The model takes a sequence of tokens (partial sentence or context) as input and generates one token at a time as output, continuing to generate until a stop condition is met (like an end-of-sequence token).
- **Autoregressive Property**: A key feature of decoder-only models is that they are **autoregressive**. This means they generate text one token at a time, conditioning each token on the previously generated tokens. These models excel at tasks like text generation, where predicting the next word in a sequence is important.

An example of a decoder-only model is **GPT (Generative Pretrained Transformer)**. Models like GPT-3 and GPT-4 use this architecture, where they predict and generate text based on the input context without needing an encoder.

In contrast, **encoder-decoder** models are typically used in tasks like translation, where an input sequence (source language) needs to be transformed into a different output sequence (target language).