Machine-Learned Language Models Which Generate Intermediate Textual Analysis in Service of Contextual Text Generation

A computer-implemented method for training a machine-learned model to generate intermediate tokens for processing by an attention mechanism of the machine-learned model to improve subsequent outputs of the machine-learned model, the method comprising:

• obtaining, by a computing system comprising one or more computing devices, a training example for training the machine-learned model to perform a contextual content generation task, wherein the training example comprises:
• an initial portion comprising a context sequence;
• an intermediate portion comprising an intermediate sequence;
• and
• a response portion comprising a response sequence;
• processing, by the computing system, the initial portion using the machine-learned model, wherein the machine-learned model uses the attention mechanism to perform attention over the initial portion;
• generating, by the computing system and based on performing attention over the initial portion with the attention mechanism, one or more intermediate token predictions;
• determining, by the computing system and using the intermediate sequence, one or more first loss values for the one or more intermediate token predictions;
• generating, by the computing system and based on performing attention over the initial portion and the intermediate portion with the attention mechanism, one or more response token predictions;
• determining, by the computing system and using the response sequence, one or more second loss values for the one or more response token predictions;
• and
• training, by the computing system, the machine-learned model based on the one or more first loss values and the one or more second loss values.

A computing system for training a machine-learned model to generate intermediate tokens for processing by an attention mechanism of the machine-learned model to improve subsequent outputs of the machine-learned model, the computing system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising:
• obtaining a training example for training the machine-learned model to perform a contextual content generation task, wherein the training example comprises:
• an initial portion comprising a context sequence;
• an intermediate portion comprising an intermediate sequence;
• and
• a response portion comprising a response sequence;
• processing the initial portion using the machine-learned model, wherein the machine-learned model uses the attention mechanism to perform attention over the initial portion;
• generating, based on performing attention over the initial portion with the attention mechanism, one or more intermediate token predictions;
• determining, using the intermediate sequence, one or more first loss values for the one or more intermediate token predictions;
• generating, based on performing attention over the initial portion and the intermediate portion with the attention mechanism, one or more response token predictions;
• determining, using the response sequence, one or more second loss values for the one or more response token predictions;
• and
• training the machine-learned model based on the one or more first loss values and the one or more second loss values.

One or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more processors, cause a computing system to perform operations for training a machine-learned model to generate intermediate tokens for processing by an attention mechanism of the machine-learned model to improve subsequent outputs of the machine-learned model, the operations comprising:

• obtaining a training example for training the machine-learned model to perform a contextual content generation task, wherein the training example comprises:
• an initial portion comprising a context sequence;
• an intermediate portion comprising an intermediate sequence;
• and
• a response portion comprising a response sequence;
• processing the initial portion using the machine-learned model, wherein the machine-learned model uses the attention mechanism to perform attention over the initial portion;
• generating, based on performing attention over the initial portion with the attention mechanism, one or more intermediate token predictions;
• determining, using the intermediate sequence, one or more first loss values for the one or more intermediate token predictions;
• generating, based on performing attention over the initial portion and the intermediate portion with the attention mechanism, one or more response token predictions;
• determining, using the response sequence, one or more second loss values for the one or more response token predictions;
• and
• training the machine-learned model based on the one or more first loss values and the one or more second loss values.