Since the architecture processes words simultaneously rather than sequentially, how do know the order of words in a sentence? If I scramble a prompt, how does the underlying mechanism differentiate the structure without built-in recurrent loops?
3 answers
Without a fix, the attention mechanism would treat a sentence as a bag of words with no order. To solve this, developers add positional encodings directly to the input word embeddings. These encodings use unique mathematical functions, often sine and cosine waves of different frequencies, to generate a distinct numerical pattern for each position index. By summing these vectors with word embeddings, the model gets unique location context along with semantic meaning, allowing the network to preserve exact word order during parallel matrix operations.
Are you interested in fixed sinusoidal positional vectors, or are you exploring learnable absolute and relative positional embeddings that adapt dynamically during model training?
They use mathematical wave formulas to inject a unique sequence coordinate into each word vector before the data hits the main attention blocks.
Well put, Janet. Adding those coordinates directly to the initial embeddings ensures that word order is never lost during massive parallel calculations.
Stephen, I am analyzing how fixed sinusoidal functions handle varying sequence lengths compared to learnable embeddings. I want to see if fixed geometric curves generalize better when an application encounters test prompts that are much longer than anything seen during training phases.