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Paperium
Paperium

Posted on • Originally published at paperium.net

Entropy-based Pruning of Backoff Language Models

Trim Language Models with Entropy: Smaller, Faster, Same Accuracy

Imagine shrinking a big language system down to a quarter of its size, while it still recognizes speech just the same.
Researchers used a simple idea called entropy to find which short word patterns really matter and which dont.
By quietly removing the patterns that change the model almost not at all, they practiced smart pruning that keeps behavior steady.
The result was a model about 26% its original size, a much smaller model that runs faster and uses less memory, yet keeps no loss in accuracy for recognition tasks.
They also checked this way against an older shortcut method, both picked many of the same patterns, but the entropy approach did a bit better.
What that means is apps, phones, and services could use powerful language tools without needing huge storage or slowdowns, making them more practical for everyday use.
This lets big models be useful, not just big, and easier to run where you already are.

Read article comprehensive review in Paperium.net:
Entropy-based Pruning of Backoff Language Models

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