Microsoft Technology Licensing, LLC

Brian Paul Slininger

Andrew Donald Gordon

Advait Sarkar

Carina Suzana Negreanu

Benjamin Zorn

Neil Toronto

Sruti Srinivasa Ragavan

Christian Leopold Bejamin Poelitz

The indirect querying of models to determine capabilities possessed by the model. Such indirect queries take the form of model input that potentially includes a natural language input user data. Such model input is structured such that the output of the model is either not natural language at all, or else is natural language that is not semantically responsive to the natural language input. Nevertheless, the output is evaluated to estimate or determine the capability possessed by the model. Thus, models may be more fully utilized to their better potential.

Model capability extraction

The generation of a response to a task prompt that represents a task to perform on declarative code. The response is generated with the aid of a language model that was trained on imperative code. The declarative code includes declarations about data. A task prompt represents a task to perform on the declarative code. At least a portion of the declarative code and at least a portion of the task prompt are converted into input imperative code. The input imperative code is then caused to be provided as input to the language model, resulting in the language model generating output imperative code. At least a portion of the output imperative code is then converted into a response to the task prompt.

Responding to task prompt on declarative code using language model

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Brian Paul Slininger