Understanding Positional Encodings in LLMs with Intuitive Explanation #PositionalEncodings

Positional Encodings in LLMs Intuitively Explained | by Charles Chi | AI: Assimilating Intelligence | Jun, 2024

Positional vectors are crucial for helping large language models understand the order of words in a sequence. By using functions to generate positional encodings, unique patterns are created to enable models to distinguish between different positions. This process ensures that each word and its position are represented, maintaining both meaning and context for language processing.

The example provided illustrates how positional encodings are calculated for a simple 3-word sentence using a 5-dimensional model. Sinusoidal functions are employed to generate these encodings, showing how each position in the sequence is uniquely encoded.

Expanding on the example, embedding vectors are introduced and positional encodings are added to them using numpy. The process of generating positional encodings, simulating dense embeddings, and combining them is demonstrated with a longer sentence.

Overall, positional encodings play a critical role in modern AI models using the Transformer architecture, allowing them to effectively handle sequential data. This addition enhances the sophistication of how AI models process and comprehend language, paving the way for their application in diverse and complex tasks.

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