Multi-Turn Collaboration For Machine-Learned Inference

One or more non-transitory computer-readable media storing instructions that are executable by one or more processors to cause a computing system to perform operations, the operations comprising:

• receiving a first input descriptive of requested content to be generated via a machine-learned inference operation of a generative machine-learned model;
• generating, based on the first input, structured data indicative of one or more target output properties for the machine-learned inference operation of the generative machine-learned model, the one or more target output properties being unspecified by the first input;
• presenting the structured data to a user via a graphical user interface comprising one or more components configured to enable the user to modify the structured data;
• receiving, via the graphical user interface, one or more second inputs indicative of one or more changes to the one or more target output properties;
• updating the structured data indicative of the one or more target output properties based on the second input to generate updated structured data;
• and
• generating, using the generative machine-learned model and based at least in part on the updated structured data, an output.

A computing system comprising one or more processors and one or more non-transitory computer-readable media storing instructions that are executable by one or more processors to cause the computing system to perform operations, the operations comprising:

• receiving a first input descriptive of requested content to be generated via a machine-learned inference operation of a generative machine-learned model;
• identifying, based at least in part on the first input, one or more output properties that are unspecified by the first input;
• presenting, to a user based on the one or more output properties, a graphical user interface to specify the one or more output properties;
• receiving, via the graphical user interface, one or more second inputs indicative of one or more values for the one or more output properties;
• providing, to the generative machine-learned model based at least in part on the first input and the one or more values for the one or more output properties, a third input descriptive of the requested content and descriptive of the one or more values for the one or more output properties;
• and
• generating, using the generative machine-learned model and based at least in part on the third input, an output.