Tag: transformer architecture
From Rule-Based NLP to Large Language Models: A 30-Year Evolution
Trace the 30-year evolution of NLP from brittle rule-based systems to powerful Large Language Models. Discover how statistical methods and transformer architectures revolutionized AI.
Feedforward Networks in Transformers: Why Two Layers Boost LLMs
Discover why the two-layer Feedforward Network (FFN) remains the backbone of Large Language Models despite debates over redundancy. We analyze parameter efficiency, hardware constraints, and recent research challenging the status quo.
Autoregressive Text Generation in LLMs: How Next-Token Prediction Works
Discover how autoregressive text generation powers Large Language Models through next-token prediction. Learn about causal language modeling, decoding strategies, and the technical trade-offs of sequential generation.
Rotary Position Embeddings (RoPE) vs ALiBi: How Modern LLMs Handle Sequence Order
Explore the differences between Rotary Position Embeddings (RoPE) and ALiBi, two critical techniques enabling modern LLMs to handle long contexts and sequential data efficiently.
