Understanding Large Language Models
Large Language Models (LLMs) represent one of the most significant advances in AI and natural language processing in recent years. Still, many pressing questions about their mechanisms, capabilities, and relationship to human cognition remain highly debated. This chapter aims to outline our current understanding of LLMs by discussing recent evidence on emerging capabilities and their mechanistic implementation within processing layers. We begin with a concise overview of the Transformer architecture, emphasizing how the attention mechanism enables training on massive datasets, allowing LLMs to
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via unknownTransformer →
- LinkedLinked via unknownNew Server Hopes to Break Through AI’s “Memory Wall” →
- LinkedLinked via unknownIdentifying Interactions at Scale for LLMs →
- LinkedLinked via unknownBreakthrough in long-context efficiency announced →
- LinkedLinked via unknownIEEE Rolls Out Large Language Models Virtual Training Course →
- LinkedLinked via unknownattention-zoo →
- LinkedLinked via arxiv author · 85%Yannik Keller →
“Understanding Large Language Models”
- LinkedLinked via arxiv author · 85%Thomas Eisenmann →
“Understanding Large Language Models”