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Applying language models to natural language processing tasks typically relies on the representations in the final model layer, as intermediate hidden layer representations are presumed to be less informative.
Perplexity—a measure of the difficulty of speech recognition tasks
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Earlier work this paper cites.
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A simple hash-based early exiting approach for language understanding and generation
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