data_analysis:statistics_and_machine_learning

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

data_analysis:statistics_and_machine_learning [2024/07/05 05:52] – [유사도 Encoder] prgramdata_analysis:statistics_and_machine_learning [2025/07/07 14:12] (current) – external edit 127.0.0.1
Line 828: Line 828:
 [[data_analysis:GPT]] [[data_analysis:GPT]]
 - 디코더만 사용 - 디코더만 사용
 +- Large model이면 디코더만 있어도 decoder-only models can internally encode necessary context from prior tokens, reducing the need for an explicit encoder.
 +- Decoder-only models still leverage self-attention (though in a causal form), allowing them to capture dependencies efficiently.
 +- Many tasks that traditionally required encoder-decoder architectures can now be handled end-to-end with a sufficiently large decoder-only model.
 +- With enough training data, a decoder-only model learns latent representations of input text similar to an encoder but in a more flexible, autoregressive way.
  
 === 유사도 Encoder === === 유사도 Encoder ===
  • data_analysis/statistics_and_machine_learning.1720158742.txt.gz
  • Last modified: 2025/07/07 14:12
  • (external edit)