位置编码 — 深入探讨 + 问题：盒状模糊

Positional encodings are a critical component of the architecture of Large Language Models (LLMs), enabling these models to capture the sequential nature of input data such as text or time series. The primary function of positional encodings is to incorporate information about the position of each token in the input sequence, which is essential for tasks that depend on the order of elements, such as language translation or text summarization. By effectively encoding positional information, LLMs can better understand and generate natural language, enhancing their performance across various applications.