Tag: transformer architecture
How LLM Attention Works: Key, Query, and Value Projections Explained
Explore how Key, Query, and Value matrices drive attention in LLMs. Understand their roles, math, and impact on AI performance with clear explanations and practical insights.
- May 3, 2026
- Collin Pace
- 0
- Permalink
Sliding Windows and Memory Tokens: Extending LLM Attention
Explore how Sliding Window Attention and Memory Tokens extend Large Language Model capabilities. Learn about transformer design optimizations that balance computational efficiency with long-context understanding.
- May 1, 2026
- Collin Pace
- 0
- Permalink
How Large Language Models Handle Many Languages: Multilingual NLP Progress
Explore how Large Language Models use cross-lingual alignment and the 'English bridge' to process multiple languages, bridging the gap for low-resource tongues.
- Apr 28, 2026
- Collin Pace
- 6
- Permalink
Feedforward Networks in Transformers: Why Two Layers Boost Large Language Models
Feedforward networks in transformers are the hidden force behind large language models. Despite their simplicity, the two-layer design powers GPT-3, Llama, and Gemini by balancing depth, efficiency, and stability. Here’s why no one has replaced it.
- Mar 18, 2026
- Collin Pace
- 5
- Permalink
How Context Windows Work in Large Language Models and Why They Limit Long Documents
Context windows limit how much text large language models can process at once, affecting document analysis, coding, and long conversations. Learn how they work, why they're a bottleneck, and how to work around them.
- Feb 23, 2026
- Collin Pace
- 0
- Permalink
Contextual Representations in Large Language Models: How LLMs Understand Meaning
Contextual representations let LLMs understand words based on their surroundings, not fixed meanings. From attention mechanisms to context windows, here’s how models like GPT-4 and Claude 3 make sense of language - and where they still fall short.
- Sep 16, 2025
- Collin Pace
- 0
- Permalink