Ever wonder how machines understand the sequence of words in a sentence?
Image: Software: xAIScreenshot:VulcanSphere, Public domain, via Wikimedia Commons
weight tying does in language models: shares embedding and output projection matrices
Ever wonder how machines understand the sequence of words in a sentence?
Imagine you're trying to learn a new language by listening to a conversation. You struggle to remember the order of words and phrases because they don't always come in neat, isolated chunks.
Think of a conversation as a flowing river. Traditional language models can't always remember the path back to where they started (long-range dependencies). RNNs, with their memory-like hidden states, keep track of the river's flow, remembering past words to understand the whole sentence.
Example
If you hear "I went to the store, and I bought some apples," a traditional model might forget "I went to the store" when processing "I bought some apples."
Remember this
RNNs use hidden states to remember previous words, helping them understand sequences like sentences.
Text adapted from Wikipedia, licensed under CC BY-SA 4.0.
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