[tedd4u]

You train the model then do a baseline evaluation. Then you evaluate many variants where you have removed or nulled out different layers or chunks of the model. By comparing the performance of those mutated models to the baseline you can learn a lot about the model. What parts don't have much value and can be removed, the location of "functions" or "facts." Etc. Google it.

[tintor]

How come they didn’t ablate encoder? OpenAI GOT models are decoder only.

[HarHarVeryFunny]

It was originally built as a general purpose sequence-to-sequence (seq2seq) model.

The research history leading up to this was interesting - there had been a bunch of work, in various domains, on "autoencoder" architectures used to learn compact representations for things like dimensionality reduction and sequence representation. The idea was to have an encoder-decoder pair, connected by a limited bottleneck representation, with the training goal of the decoder reconstructing the encoder input from the bottleneck representation.

One example of this was to learn a fixed size(!) sequence (e.g. sentence) representation using an LSTM-based autoencoder (LSTM->embedding->LSTM), which at the time seemed rather shocking - the ability to represent a variable length sequence with a fixed size embedding. Equally shocking was that you could use this for machine translation simply by connecting an LSTM encoder for one language to an LSTM decoder for another language.

This type of LSTM->LSTM seq2seq encode-decode architecture for machine translation was then improved by Bahdanau by replacing the fixed size representation with an attention mechanism so the decoder could learn to be more specific about input-output relationships.

This type of LSTM-based seq2seq encode-decode architecture, using attention, is what Uszkoreit et al set out to improve - to make more efficient by using a parallel vs sequential (RNN) architecture. The Transformer was never conceived of as purely for language modelling, or as an "AI" architecture. Later when the usage focused on language modelling (generation, not translation), the encoder was dropped since input and output are the same thing.

[mike_hearn]

If you read the Wired article linked elsewhere on this thread, then it explains that. The work was being done by people from the Google Translate team.