Understanding Transformers and Language Models

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Match the following tasks with the type of language model used:

Predicting next word in a sentence = Auto-regressive Models Comprehensive understanding and encoding of entire sequences of tokens = Auto-encoding Models Natural Language Generation = Auto-regressive Models Natural Language Understanding = Auto-encoding Models

Match the following language models with their characteristics:

GPT Family = Predicting a future token given either the past tokens or the future tokens but not both BERT = Learning representations of the entire sequence by predicting tokens given both the past and future tokens Auto-regressive Models = Goal is to predict a future token given either the past tokens or the future tokens but not both Auto-encoding Models = Learning representations of the entire sequence by predicting tokens given both the past and future tokens

Match the following models with their primary usage:

GPT Family = Natural Language Generation BERT = Natural Language Understanding Auto-regressive Models = Predicting next word in a sentence Auto-encoding Models = Comprehensive understanding and encoding of entire sequences of tokens

Match the following goals with the type of language model used:

<p>Predicting a future token given either the past tokens or the future tokens but not both = Auto-regressive Models Learning representations of the entire sequence by predicting tokens given both the past and future tokens = Auto-encoding Models Predicting a next word in a sentence = Auto-regressive Models Learning representations of the entire sequence = Auto-encoding Models</p> Signup and view all the answers

Match the following tasks with the type of language model used:

<p>Auto-complete = Auto-regressive Models Natural Language Understanding = Auto-encoding Models Natural Language Generation = Auto-regressive Models Comprehensive understanding and encoding of entire sequences of tokens = Auto-encoding Models</p> Signup and view all the answers