<ul><li>Traditional models like RNNs and LSTMs process data sequentially, while Transformers use self-attention mechanisms to process all tokens simultaneously, requiring positional encoding to maintain positional information.</li><li>Different positional encoding schemes exist, including sinusoidal encoding, trainable embeddings, relative positional encoding, and Rotary Positional Embedding (RoPE), each with unique benefits for model performance and generalization.</li><li>Sinusoidal encoding allows models to attend based on relative positions, while learned embeddings and relative positional encoding focus on learning relative distances between tokens for improved natural language understanding.</li><li>New positional encoding methods are continuously being explored to enhance LLM performance, interpretability, and scalability, playing a crucial role in advancing the next generation of language technologies.</li></ul>

Understanding Positional Encoding in Transformer and Large Language Models

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