<ul><li>A new approach to model compression by merging similar parameter groups within a model has been proposed.</li><li>The method involves selecting, aligning, and merging separate feed-forward sublayers in Transformer models.</li><li>The approach has been tested on language modeling, image classification, and machine translation tasks.</li><li>The results show that the method achieves comparable performance to the original models while removing a significant portion of parameters.</li></ul>

Merging Feed-Forward Sublayers for Compressed Transformers

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