Tag: positional encoding
Positional Information in LLMs: How Transformers Understand Word Order
Discover how Large Language Models understand word order despite parallel processing. We compare Absolute Position Embeddings, RoPE, and new techniques like positional memory.
Rotary Position Embeddings (RoPE) vs ALiBi: Which LLM Positioning Method Wins?
Compare RoPE and ALiBi positional embeddings in LLMs. Learn how rotation matrices and linear biases solve the context window problem for models like Llama.
Positional Encoding in Transformers: Sinusoidal vs Learned for Large Language Models
Sinusoidal and learned positional encodings were the original ways transformers handled word order. Today, they're outdated. RoPE and ALiBi dominate modern LLMs with far better long-context performance. Here's what you need to know.
