Encoder? Decoder? Why LLMs Uses Neither Or Just One?
📰 Medium · LLM
Learn why modern LLMs often use only one half of the original transformer architecture and how this impacts their functionality
Action Steps
- Read the original transformer paper to understand the dual-half architecture
- Analyze how modern LLMs have modified this architecture to use only one half
- Experiment with implementing a single-half transformer model using popular libraries like PyTorch or TensorFlow
- Compare the performance of single-half and dual-half transformer models on a benchmark task
- Evaluate the trade-offs between using an encoder-only or decoder-only architecture in LLMs
Who Needs to Know This
NLP engineers and AI researchers can benefit from understanding the evolution of transformer architecture and its implications on LLM design
Key Insight
💡 Modern LLMs often use only one half of the original transformer architecture, either the encoder or decoder, to improve efficiency and performance
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🤖 Did you know most modern LLMs ditched the dual-half transformer architecture? 📚 Learn why and how it affects their performance
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