Generative AI with LLMs: Autoencoder: A Transformer-Based Model Architecture for Guessing Masked Tokens

Learn what an autoencoder is and how it can guess masked tokens based on the previous sequence of tokens by building bidirectional representations of the input sequence using the transformer architecture.

Question

Which transformer-based model architecture has the objective of guessing a masked token based on the previous sequence of tokens by building bidirectional representations of the input sequence.

A. Autoregressive
B. Sequence-to-sequence
C. Autoencoder

Answer

C. Autoencoder

Explanation

The correct answer is C. Autoencoder. An autoencoder is a type of transformer-based model architecture that has the objective of guessing a masked token based on the previous sequence of tokens by building bidirectional representations of the input sequence. An example of an autoencoder model is BERT, which stands for Bidirectional Encoder Representations from Transformers.

An autoencoder model works by taking an input sequence, such as a sentence, and randomly masking some of the tokens, such as words, with a special symbol. The model then tries to reconstruct the original sequence by predicting the masked tokens based on the context provided by the unmasked tokens. This way, the model learns to encode the input sequence into a latent representation that captures its meaning and structure. The latent representation can then be used for various downstream tasks, such as text classification, question answering, or sentiment analysis.

An autoencoder model differs from an autoregressive model, which predicts the next token in the sequence based on the previous tokens, such as GPT, or a sequence-to-sequence model, which transforms an input sequence into an output sequence, such as T5. An autoencoder model can leverage both the left and the right context of the input sequence, while an autoregressive model can only use the left context. An autoencoder model can also learn from the entire input sequence at once, while a sequence-to-sequence model needs to process the input and output sequences sequentially.

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